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In the field of hydraulic engineering, the design of pumping stations played an essential role in ensuring the efficient and reliable operation of water supply systems. However, due to various factors such as asymmetric pump configurations and non-straight alignment of the forebay and inlet pool center lines, the flow patterns in the forebay could be poor, which could lead to problems such as cavitation and reduced pump efficiency. In order to improve the unfavorable flow patterns within the pump station's inlet structure and enhance the efficiency of the pump operation, this study focuses on optimizing the structural design parameters of the forebay's sill using computational fluid dynamics and the BPNN-GA (back propagation neural network-genetic algorithm) model. To facilitate the genetic algorithm's fitness calculation, a comprehensive evaluation index F for the flow patterns in the forebay is proposed based on the uniformity of axial velocity distribution and velocity-weighted average angle. By taking the comprehensive evaluation index F as the objective parameter, the BPNN is optimized using the genetic algorithm, leading to the determination of the optimal sill design parameters, which are then compared and analyzed against the numerically simulated structures from the orthogonal experimental design.The research findings indicate that, under the operation of pumps No.1, No. 2, and No.4, compared to the optimal design from the orthogonal experimental design, the uniformity of axial velocity distribution of No.1 pump inlet flow increased by 16.58 percentage points, and the velocityweighted average angle increased by 4.66°. For No.2 pump, the uniformity of axial velocity distribution increased by 0.49 percentage points, while the velocity-weighted average angle decreased by 2.81°. As for No.4 pump, the uniformity of axial velocity distribution improved by 8 percentage points, and the velocity-weighted average angle increased by 7.81°, resulting in a comprehensive evaluation index F of 1.16, indicating a significant improvement in the flow patterns of the forebay.The optimization of the pump station's forebay sill parameters using the BPNN-GA algorithm overcomes the drawback of traditional methods being trapped in local optima. It enables the identification of the optimal sill design parameters for the uniformity of axial velocity distribution and velocity-weighted average angle within the required design range, providing reference for the application of computational intelligence in optimizing hydraulic design in pump stations. [ABSTRACT FROM AUTHOR]

Aerobic compost has been commonly used to efficiently dispose of resources recycling and environmental protection in modern agriculture. Among them, aeration can be one of the most important environmental factors to affect composting fermentation. It is necessary for a feasible network model to accurately control the oxygen supply of aeration. This study aims to improve the aeration efficiency and prediction accuracy of aerobic composting aeration. In-depth learning was selected to train a network model, in order to predict the oxygen supply of aeration during aerobic composting fermentation in this experiment. Raw materials were taken as cow dung, cow dung biogas residue, chicken manure, and corn straw in the Haihua Biogas Plant in Miyun District, Beijing, China. The corn straw was crushed by 1-2 cm in grain size. The cow dung, cow dung biogas residue, and chicken manure were uniformly mixed with the crushed corn straw for composting and fermentation. The sensor was used in the composting fermentation tank to collect the parameter data during aerobic fermentation. 268 groups of data were selected as the sample data, 218 groups of data were randomly selected as the input data, and 50 groups of data were selected as the test data. Clonal genetic algorithm (CGA) was used to predict the standard back propagation (BP) neural network model for the aeration oxygen supply, whereas, the 6-14-1 three-layer network structure was used as the basic structure of the prediction model. The input parameters were the temperature, humidity, oxygen concentration, room temperature, pH value, and electrical conductivity (EC). The mean square error (MSE) of the number of hidden layer nodes was determined to be 14 after training and calculation. The output data was aeration. This article establishes BP neural network model for predicting aeration oxygen supply. Then the genetic algorithm (GA) and clonal selection algorithm were used to improve the prediction accuracy of the model. The experiment shows that the CGA-BP neural network model has the best prediction effect on aeration oxygen supply. 1) The CGA-BP neural network model accelerated the obtaining of the optimal solution, with an efficiency improvement of 75.36% and 51.30% compared to the BP model and GA-BP model, respectively. 2) In the prediction model of aeration oxygen supply, the CGA-BP model had a more accurate prediction effect, with a prediction accuracy of 99.65%. The prediction accuracy of aeration oxygen supply was 96.99% and the prediction accuracy of the GA-BP neural network model reached 99.26%. A comparison was made to evaluate the errors of BP, GA-BP and CGA-BP neural models. The model evaluation showed that the best performance was found in the CGA-BP neural network model with the smallest error, as shown by the mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE). 3) The improved CGA-BP neural network model can be predicted the aeration oxygen supply of aerobic composting, increasing the aeration control efficiency by 3.22%. The improved model can be expected to accurately predict the aeration oxygen supply of aerobic compost. The finding can provide a strong reference for accurate data for the next aeration. [ABSTRACT FROM AUTHOR]

Accurate monitoring of soil moisture content (SMC) in agricultural fields can greatly contribute to the utilization of water resources and sustainable development. Low SMC can cause the soil to harden during crop growth, which in turn affects the absorption of the crops' water and nutrients. In this study, the field-scale wheat canopy spectra were gathered to quantitatively estimate and map the SMC using an unmanned aerial vehicle (UAV) hyperspectral sensor. The study area was selected as Fukang City, Xinjiang Uygur Autonomous Region, China (87°51'15′′E, 44°21'14′′N) at the transition zone between a desert and an oasis. The soil samples were also collected concurrently with the hyperspectral data. 70 sampling units of 0.5 m×0.5 m were evenly selected in the target field, where the GPS locations were recorded. The wheat canopy spectra were smoothed using the savitzky-golay (SG). The spectral data were then transformed using the continuous wavelet transform (CWT) with seven different wavelet functions (bior4.4, coif4, db4, fk14, haar, rbio3.9, and sym 4). The wavelet coefficients were extracted by the genetic algorithm (GA), and finally combined with partial least squares (PLSR), Support Vector Machine (SVM), artificial neural network (ANN), radom forest (RF), and extreme gradient boosting (XGBoost) to predict the SMC. A neural Network was used to predict the SMC, and then map the SMC at a spatial scale. The results demonstrate that the accuracy of SMC estimation greatly increased using the GA-based feature band selection. The determination coefficient (R²) of the feature band was 0.35-0.82 with the wavelet coefficients, while the R² of the complete wavelet band ranged from 0.20 to 0.44. Furthermore, the R² of SMC reached 0.82, 0.72, 0.79, 0.76, and 0.46, respectively, using PLSR, SVM, ANN, RF, and XGBoost models. The best estimation accuracy was achieved in the feature band with the db4 wavelet coefficients, compared with the rest wavelet functions. The predicted and measured values were remarkably similar. The GA significantly reduced the number of variables to maximize the feature factors in the remote sensing bands. The majority of the feature bands were retained to remove the redundant bands that frequently presented in the hyperspectral data. The more stable ANN model was achieved in the small number of sample points on the big sample, even though the accuracy was lower than that of PLSR. Consequently, the perfect estimation accuracy of SMC with excellent mapping was obtained in the continuous wavelet transform and GA feature extraction of the hyperspectral data acquired by the UAV. The improved inversion can be expected to achieve accurate SMC prediction using crop growth period spectra at the field scale. The finding can provide a strong reference for high-precision SMC monitoring using remote sensing. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is one of the most important parameters to predict the drought degree in agricultural production and precision management. In this research, the multi-model ensemble method was applied to improve the forecasting precision for ET0. Genetic algorithm-support vector regression was utilized to forecast the weather variables output by European Centre for Medium-Range Weather Forecasts, National Centers for Environmental Prediction, Japan Meteorological Agency, and Korea Meteorological Administration models. The prediction accuracy and reliability of the model were evaluated by three indicators: root mean square error (RMSE), mean absolute percentage error (MAPE), and determination coefficient (R² ). Penman-Monteith equation was used to forecast the ET0 from 1 to 7 days in the future, according to the optimal models and schemes. Finally, the applicability of the improved model was verified in agricultural test field, including the Yuncheng Station in Shanxi Province of China. The results show that the multi-mode integration improved the prediction performance of air temperature, actual water vapor pressure, and wind speed under a single mode, with the largest improvement in the daily maximum temperature, followed by the actual water vapor pressure, wind speed, and daily minimum temperature. The prediction performance showed a downward trend with the increase in the prediction period. There was no trend of multi-mode integration on the prediction performance of solar radiation. The forecast accuracy of a single model in the ET0 forecast decreased rapidly with the growth of the forecast period. By contrast, the multi-mode integration scheme improved the accuracy of ET0 forecasting and the stability in a long forecast period, and then balanced the tradeoff of weather forecasting accuracy and duration. In ET0 forecasts, the performance of the multi-model schemes was significantly better than that of the original single models. The best prediction performance was achieved in the forecasting precision of the recombination scheme with the optimal models. The smallest RMSE and MAPE were 0.65-0.81 mm/d and 19.43%~23.78%, respectively, and the highest R² was 0.83-0.89. There was excellent forecast performance of the reorganization scheme in the seasonal ET0 forecast throughout the year, except for the long forecast period in summer. The forecast performance in spring and winter was the best of all the schemes, whereas, the forecast accuracy in autumn was also maintained at a high level. The reorganization scheme still showed better prediction performance in the ET0 prediction of the test field in the next 1-7 days, with the RMSE and the average absolute percentage error not exceeding 0.83 mm/d and 34.57%, respectively. The adaptability of numerical weather forecasts can be verified in the township areas under Yuncheng Station, providing accurate ET0 forecast information for local agricultural production. It was of great significance for agricultural water demand prediction and optimal management of water resources. However, the ET0 prediction performance and applicability of the multi-mode integration scheme need to be verified for the other agricultural production areas and seasons. [ABSTRACT FROM AUTHOR]

Assessing the sustainable development of agriculture is a hot topic in global agricultural research and an important task of rural revitalization and agricultural upgrading in the new era. Reform and opening up promote the rapid development of the agricultural economy, but the conflict between agricultural development and agricultural resources and ecological environment is serious. Since the 18th National Congress of the CPC, the sustainable development of agriculture has been given more prominence. It is of great significance to study the evaluation of agricultural sustainable development. Firstly, this paper studies the relevant literature on agricultural sustainable development evaluation at home and abroad. Based on wavelet transform, support vector machine(SVM), genetic algorithm (GA)and ant colony optimization(ACO) algorithm, a combination evaluation method of wavelet and intelligent optimization support vector machine is proposed. Secondly, by referring to the existing evaluation index system and combining with the actual situation of China's agricultural development, the alternative index set of agricultural sustainable development evaluation is established. Using SPSS software, the significance and correlation of 150 primary indexes are analyzed, and an evaluation index system including 62 indexes is established. Thirdly, using one-dimensional discrete stationary wavelet analysis, data denoising processing, genetic algorithm, ant colony algorithm, optimize the parameters of support vector machine, get better penalty parameters, kernel function, insensitivity coefficient, and then train the support vector machine. Fourth, taking 31 provinces (municipalities) in China as an example, this paper evaluates the agricultural sustainable development by applying the wavelet-GASVM model and the wavelet-ACOSVM model, and using the GA-SVM model and the ACO-SVM model to evaluate the agricultural sustainable development of 31 provinces (municipalities) in China. Fifthly, the reliability of the wavelet intelligent optimization SVM method is verified by changing the number of test sets and conducting multiple tests. The evaluation and simulation results show that: 1) The mean square error of agricultural sustainable development evaluation of Wavelte-GASVM is 9.641×10-5, and the correlation coefficient is 0.980; however, under the same training set and test set, the mean square error and correlation coefficient obtained by GASVM are 0.006 and 0.979, respectively. 2)The mean square error of Wavelet-ACOSVM agricultural sustainable development evaluation was 9.318×10-5, and the correlation coefficient was 0.972; the mean square error MSE and correlation coefficient of ACO-SVM are 0.016 and 0.953, respectively, under the same training set and test set. 3) Change the number of test sets and perform several tests and compare the results of GASVM model and ACO-SVM model before and after wavelet denoising respectively. It can be seen that under the GASVM model, the mean square error of the running results after wavelet denoising is smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Under the ACOSVM model, the mean square error of the running result after wavelet denoising is also smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Conclusion: The prediction accuracy and convergence speed of agricultural sustainable development evaluation by wavelet-GASVM and wavelet-ACOSVM are better than GA-SVM and ACO-SVM, respectively, and the effect of agricultural sustainable development evaluation by wavelet-GASVM is more ideal. This study provides a method reference for agricultural sustainable development assessment. [ABSTRACT FROM AUTHOR]

Circular monorail transportation system has been widely applied in hilly mountain areas. It is a high demand to improve the operation efficiency of static tasks in recent years. This study aims at the matching and scheduling requirements between mission points with the transport needs and transporters. The maximum full load was also considered comprehensively to determine the practice scenario of one-way goods transportation. The task was in the form of transporting cargo boxes from several loading points to a single unloading point. A mathematical model was established with the "carpooling" combined with the processing at the mission points and task assignment of transporters. The solving process of the transporter schedule was divided into two stages. In the first stage, the heuristic rule algorithms using different priorities were proposed to solve the "carpooling" combined processing with the goal of the least number of loadings of transporters. Combination algorithms included prioritization by adjacent position, task volume, and random sequence. In the second stage, a genetic algorithm based on variable neighborhood search (GA_VNS) was employed to solve the task assignment of transporters with the goal of the least number of transporter jams. GA_VNS was a hybrid intelligent algorithm with GA as a framework with the variable neighborhood search strategy, particularly with both global and local optimization. The performance of GA_VNS was compared with the standard GA, variable neighborhood search algorithm, overall matching rule, and random-restart hill-climbing algorithm. Transporting situations were simulated corresponding to three kinds of task batches. Five control experiments were carried out for each batch to improve the reliability of the experimental data. Experiments proved that the selected algorithms were superior. The designed combination algorithm with the task volume as the priority was solved fastest and was independent of the task batch. The operation time was generally between 0.3 and 0.4 s, with the lowest time cost. The feasible solution in 80% of the experiments was optimal among the three combination algorithms, and the solution advantage was concentrated in the large batch experiment, indicating more suitable for solving large-scale transportation in real time than the rest. The number of loadings in the remaining experiments was also only one unit more than the optimal solution. The solution of the assignment was also facilitated for the second stage scheduling in the validity. Therefore, the best performance was achieved in the combination algorithm with the task volume as a priority. The GA_VNS was employed to optimize the solution in multiple directions. GA_VNS significantly improved the quality of standard GA solution, and the number of transporter jams was reduced by 33.3%-100%. The higher stability was found in large-batch transporting situations, compared with the single variable neighborhood search algorithm. The probability of the minimum number of transporter jams appearing increased by 10% and 40%, but the operation time was about 10 times longer. The validity was better than other types of algorithms, such as overall matching and random-restart hill climbing. The finding can provide an approximately optimal solution to the static scheduling of the circular monorail transportation system in hilly mountain areas. [ABSTRACT FROM AUTHOR]

Fresh fruits and vegetables need transportation and storage after harvesting, due to the regional and seasonal production in recent years. It is a high demand to clarify the relationship between environmental factors and the sensory quality of fresh fruits and vegetables during transportation. Accurate and rapid evaluation and prediction can be of great significance to maintain the logistics and sensory quality of fresh fruits and vegetables using environmental factors. Taking the table grape as the research object, the transportation simulation and sensory experiments were carried out in the laboratory, according to the tracking and monitoring actual transportation process. A data set of sensory quality was then constructed for the table grape during transportation. A prediction model was established for the environmental factors (temperature, relative humidity) during transportation and sensory quality (appearance, aroma, texture and flavor) using the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with the Multiple Output Support Vector Regression (PSOGA-MSVR) model. Specifically, the SVR model was widely used to identify the small sample, nonlinear, and high-dimensional pattern in the field of food quality prediction. The accuracy and generalization of SVR model depended largely on the kernel function and hyperparameters. The Radial Basis Function (RBF) was selected as the kernel function in this case. The results show that the PSOGA joint optimization effectively improved the parameter adjustment efficiency of the MSVR model. The improved PSOGA-MSVR model performed better prediction under three transportation modes, including normal, cold temperature, and cold chain transportation. Among them, the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R² ) of the model were 0.083, 0.029, 0.172, and 0.982, respectively, under the normal temperature transportation. The MAE, MSE, RMSE, and R² were 0.077, 0.022, 0.148, and 0.981, respectively, under cold storage and transportation. The MAE, MSE, RMSE, and R² of cold chain transportation were 0.063, 0.018, 0.138, and 0.985, respectively. Therefore, cold chain transportation achieved the highest prediction accuracy for the sensory quality of table grapes. The better-fitting model was attributed to the longest time series and the most abundant data in the cold chain data set. There was a significant nonlinear relationship between transportation environmental factors and grape sensory quality (appearance, aroma, texture and flavor). Consequently, reasonable regulation of transportation environmental factors can be expected to effectively maintain the sensory quality of fresh fruits. The SVR model can also be used to simulate the logistics environmental factors and grape sensory quality of fresh table grapes. The quality evaluation and prediction can provide a theoretical, technical, and practical solution to improve the logistics process and quality of fresh table grapes. [ABSTRACT FROM AUTHOR]

Drip irrigation has been one of the most effective water-conserving irrigation technologies in the arid and semiarid areas. Among them, the rotational irrigation mode has been the mainstream application in Xinjiang, Western China. The Rotation Irrigation Groups (RIGs) have been widely designed to deal with the water shortage in the farmlands irrigation. However, the RIGs arrangement can often be taken several days to calculate in large-scale projects, according to the experience or EXCEL software. Furthermore, these traditional approaches cannot acquire the reasonable solution. Therefore, a single-objective model was previously proposed to verify the feasibility of the intelligent algorithm on the manual drip irrigation. In this study, a mathematical model was proposed to determine the minimum flow standard deviation using a gravitational search algorithm (GSRV) and Genetic Algorithm (GA) with the hybrid variable neighborhood. A systematic analysis was made to clarify the differences between the automated and manual drip irrigation, particularly for the high calculation efficiency under the intelligent algorithm. The chromosome coding and population initialization were also established to acquire the problem characteristics of automated drip irrigation, according to the dividing and dispersion index. An offspring repair algorithm was also designed to reduce the constraint and illegal solutions from the crossover and variation using the penalty function method. The selection of operator was also combined with a simulated annealing mechanism to avoid the optimization process falling into the local optimum. Since the classical GA presented the global solid search but the weak local search, two neighborhood structures were constructed using the flow and location adjustment, while a search algorithm was designed using Variable Neighborhoods Search (VNS), further to enhance the GA searchability and accuracy. As such, the different neighborhood operators were selected for the VNS to jump out of the local minima. The shaking procedure and the Local Search (LS) procedure were iteratively alternated until a predefined stop condition, thereby to escape from local optimal solutions. The specific search procedure was as follows. 1) The parameters were first initialized to calculate the population fitness, then to select the coding bit with the probability for the dynamic performance of variable neighborhood search. The next step was check the constraints to calculate the fitness value and update the population for the subsequent search. Finally, the population encoding was output until the population traversal was completed or the algorithm iterations reached. The experimental results show that the improved model was effective and universality in this case. Specifically, the minimum standard deviation of the solution was 10.03 m3/h. The solution was also converged in about 350 generations, where the branch pipes were dispersed in the different sub-main pipes. The GSRV-GA demonstrated an excellent convergence and search accuracy, which verified the effectiveness of the proposed algorithm. Three groups of cases with different sizes were selected to carry out the verification of model universality, also indicating an excellent performance. The minimum standard deviations were 15.18, 13.93, and 7.52 m3/h, respectively. The average standard deviations of shaking experiment simulating the blockage of 1-5 branch pipes were 10.50, 11.09, 11.47, 12.15, and 12.81 m3/h, respectively. The jitter simulation experiment showed the influence of branch blockage on the flow equalization of the irrigation group, indicating the robustness of the model. At the same time, the approximate standard deviations of the flow rate were no more than 1%, and the dispersion values were 1 and 0.73 for the improved model and the previous one in the actual case, respectively. It infers that the different grouping requirements of rotation irrigation group were realized in the design. Consequently, the feasibility was achieved for the RIG division of automatic drip irrigation, thereby to fully meet the hydraulic calculation and engineering requirements in modern agriculture. The finding can provide the research foundation for the subsequent mining of water-saving technology in automatic drip irrigation. [ABSTRACT FROM AUTHOR]

This study aims to realize the joint deployment of multiple agricultural machinery stations, particularly for the real-time job orders. A mathematical model with a fuzzy time window was also established to minimize the total scheduling time and the number of dispatching agricultural machinery. Some factors were comprehensively considered, such as farmers' satisfaction, the cooperation of multiple agricultural machinery stations, the number of orders, the area of farmland, and the location coordinates. An improved genetic method (GA) with excellent parent genes was designed to fulfill the task of multi machine station responding to the demand of multi farmland. At the same time, the agricultural machinery was allocated in the shortest time to implement the operation requirements of each farmland, according to the shortest path. A case study was carried out to verify the model and the visual interface, including three stations of agricultural machinery and 35 operation orders of farmland in a certain area around Wuhan, Hubei Province of China. The results showed that an excellent searching and stable convergence were achieved in the scheduling system of agricultural machinery. Specifically, the reduction rate of the total scheduling distance was 9.89%, and the reduction rate of the number of agricultural machinery was 15.38%, when the fuzzy membership degree was 0.8. An optimal number of real-time orders accepted by a single farm station was not more than 20, according to the actual situation of the agricultural machinery quantity in each station. Furthermore, the improved GA presented a better performance than the hybrid genetic in general, indicating the less calculation time of the deployment, the more reasonable allocation of tasks, and the reduced scheduling distance. The multi-site and multi-machine cooperative instant repose scheduling was also considered the joint deployment agricultural machinery and fuzzy time window in the modeling. There was a higher accuracy of the scheduling operation on agricultural machinery, and the fully considered satisfaction of farmers, even though the complexity of model increased, compared with the scheduling operation at a single agricultural machinery station. In the scheduling algorithm, the crossover and mutation operators were improved to reduce the risk of the operation data falling into the local optimal solution with the less running time. Consequently, the scheme can be widely expected to completely deal with agricultural machinery scheduling under complex backgrounds, fully meeting the cooperative operation of multiple agricultural machinery stations for the real-time operation needs of farmers. This finding can provide a strong support to the cost-saving and high efficiency of operation on agricultural machinery in modern agriculture. [ABSTRACT FROM AUTHOR]

Variable rate fertilization has generally been implemented in the field traction liquid fertilizer applicator under a variety of soil and tillage conditions in recent years. However, it is very necessary to improve the precision, even fertilization, and fertilizer saving during operation in modern mechanized agriculture. In this research, a novel fuzzy PID control was proposed using genetic algorithm (GA) for the variable rate fertilization control system of liquid fertilizer. Firstly, a closed-loop negative feedback model was established for the control system of liquid fertilizer variable rate fertilization, thereby to obtain the transfer function Gr(s) of control process. The control process was also optimized, according to the structure of traction variable liquid fertilizer applicator and the composition of electrical components. Among them, the control object was mainly an electric proportional valve in the control system. The feedback channel was read by the flow meter, and then transferred the electric signal to the controller. Specifically, the controller was implemented to compare the flow reading with the vehicle speed and the amount of fertilizer required for the current field. The obtained data was converted into the control signal, and then output to the electric proportional valve, so as to realize the negative feedback control of system. Some models were established for the traditional, fuzzy, and GA-based fuzzy PID control, according to the requirements of control system. Particularly, the fuzzy PID control model was first established before the GA-based fuzzy PID control model. The input quantity of fuzzy controller was set as the error and the error rate of change, while the output quantity was set as the compensation value of three parameters in the PID controller, where each input and output quantity was set to 7 fuzzy language values. Therefore, there were 49 fuzzy control rules in total. Subsequently, the fuzzy control rules were chromosome-coded within GA. The chromosomes of fuzzy control rules were then simulated and optimized to obtain the optimal fuzzy control rule table using genetic operators, such as selection, crossover, mutation. Correspondingly, the fuzzy PID controller was further set, according to the optimal fuzzy control rules. Finally, MATLAB software was also selected to simulate the traditional, fuzzy, and GA-based fuzzy PID control. Consequently, the response time of electric proportional valve control was 4.86 s for the variable rate fertilization control system using the GA-based fuzzy PID control, which was significantly shorter than the 8.4 s of the traditional PID control, and the fuzzy PID control of 7.32 s. An experimental platform was constructed to carry out the stability and variable control experiment for the flow control in the control system of liquid fertilizer variable rate fertilization. In addition, the flow error was measured during operation in the fertilization stability experiment. The average relative errors of control system were 5.01%, 3.41%, and 1.24%, respectively, corresponding to traditional PID control, fuzzy and GA-base fuzzy PID control during stable operation. The signal was collected and recorded for the actual vehicle speed change in the variable control experiment. The inputs were the collected vehicle speed signal to the controller through a signal generator, thereby to measure the flow when the vehicle speed changed. Consequently, the actual response times were 5.19, 4.12, and 3.21 s, respectively, corresponding to the three control modes. More importantly, the GA-based and fuzzy PID control reduced the average relative error of 3.77% and 2.17%, respectively, compared with traditional, when controlling the flow of variable rate fertilization control system. Additionally, the actual response time of GA-based and fuzzy PID reduced by 1.98 and 0.91s, compared with the traditional PID control. Anyway, the GA-based fuzzy PID control presented better response time to flow control in the variable rate fertilization control system than traditional and fuzzy PID control, indicating better operational stability. [ABSTRACT FROM AUTHOR]

The characteristics of the precious traditional Chinese medicine Fritillaria cirrhosa (FC) like humidity and fear high humidity have become the difficulty of artificial irrigation. The intelligent precision irrigation system can realize water-saving irrigation on demand. With the idea of partition variable irrigation, the partition variable irrigation system of FC was developed based on the wireless sensor network. In the process of artificial cultivation between April 2018 and December 2020, the impact of the growth age, planting season, soil and planting density on the required irrigation water of FC was investigated. The data of water demand for FC growth and irrigation water in planting soil was obtained with the help of capacitance method and soil moisture content analyzer. The better soil moisture content model for the FC growth and the suitable soil moisture content model of irrigation were established. The results showed that in addition to the factor of growth age of FC, the required soil moisture content of FC growth was related to the planting density. The required soil moisture content would rise with the increase of the planting density. The fuzzy control strategy model was established in order to achieve the application of partition variable irrigation of FC. The double-input and simple-output structure was adopted for the fuzzy controller. The input variables were the difference value which is the difference between the required soil moisture content for FC growth and the planting soil moisture content, as well as the planting density. The output variable was the increment of the irrigation soil moisture content. The fuzzy control quantification factors, the scale factor, the fuzzy control rules as well as the membership function were optimized through genetic algorithm so as to achieve the accurate decision on the irrigation water of FC. Different irrigation water was applied under conditions of different planting density, season and growth age to achieve partition variable irrigation of FC. The irrigation process was executed according to certain strategy by using the increment of irrigation soil moisture content u. The partition variable irrigation technology and system was applied in the greenhouse of FC. The experimental results showed that certain water-saving effect has been achieved through the application of fuzzy control irrigation. Based on the fuzzy control optimized by genetic algorithm, the data of irrigation water was mainly distributed between 5% to 7% and the consumption of irrigation water decreased significantly. The irrigation water under specific planting density of FC showed that error of the optimized variable irrigation was around ±5%, which satisfied irrigation demand as required water amount of FC growth. With the increase of planting density of FC, the required irrigation water also increased linearly. Evident effect has been achieved in partition variable irrigation and it was necessary for FC to adopt the strategy of partition variable irrigation. There is a parabolic relationship between water-saving rate of partition variable irrigation and the planting density ratio of FC. The optimum water-saving rate can be achieved around the standard planting density and the annual water-saving rate can reach more than 27.6%. This research can provide reference and technical support for the strategy of planting density and water-saving irrigation of FC. [ABSTRACT FROM AUTHOR]

Measurements of soil moisture have great significance for agricultural production and environmental protection. A variety of technologies have been introduced into the current monitoring of surface soil moisture, particularly widely-used optical remote sensing and synthetic aperture radar (SAR) microwave remote sensing. In this study, retrieval for surface soil moisture in farmland was proposed using multi-source remote sensing data and feature dimension reduction with GA-BP neural networks. Sentinel-1 microwave and Sentinel-2 optical remote sensing data were used with a high spatial and temporal resolution. Three field surveys and sampling were carried out simultaneously during the transit time of the Sentinel-1 satellite. A total of 20 sampling points were set on the ground in the study area to collect soil moisture in the longitude and latitude coordinates. Firstly, Sentinel-1 and Sentinel-2 data were preprocessed to extract 21 feature parameters, including 9 backscatter coefficient, 5 polarization characteristic parameters, surface roughness, and 6 vegetation indices. Then, the differential evolution feature selection (DEFS) was utilized to obtain an optimal feature subset with 10 parameters, including the incident angle, ..., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Water Band Index (WBI), and Fusion Vegetation Index (FVI). Principal component analysis (PCA) was used to further reduce the dimension of the optimal feature subset. The feature subset was reduced to eight dimensions, where an eight-dimensional feature matrix was obtained. After that, back propagation (BP) neural network was established to describe the nonlinear relationship between characteristic parameters and surface soil moisture, whereas, genetic algorithm (GA) was used to optimize the node weights and accelerated the learning speed of the BP neural network. The feature matrix after dimension reduction and some measured data of soil moisture were input into the GA-BP network for training, where the distribution map of soil moisture was obtained in the study area. Finally, taking a winter wheat field in Henan Province as the study area, three comparative experimental schemes were set to verify the accuracy of inversion using the measured data. The experimental schemes with DEFS and PCA presented the highest accuracy, where the coefficient of determination was 0.789 3, and the root mean square error was 0.028 7 cm³/cm³, compared with the genetic BP neural network, indicating the coefficient of determination increased by 0.215 7, and the root mean square error was reduced by 0.029 5 cm³/cm³. Meanwhile, the frequency distribution of soil moisture inversion was basically consistent with the measured soil moisture of sampling points. The experimental results demonstrated that the GA-BP network combining with DEFS and PCA can eliminate the redundant characteristic parameters for high inversion accuracy, and a high-resolution distribution map of surface soil moisture with a large area. The finding can offer some advantages and application potentials to the surface soil moisture retrieval in farmland. [ABSTRACT FROM AUTHOR]

A rotation irrigation is normally implemented in water-shortage regions, such as Xinjiang area of western China with widespread drip irrigation. Nevertheless, the conventional rotation irrigation group is generally divided into the fixed districts in the manual calculation. A better solution is thus highly demanding in this inefficient calculation. The rotation irrigation group is also representing the prominent regional and hydraulic characteristics in recent years. Therefore, it is necessary to clarify how to divide the rotation irrigation group in practice. Furthermore, the operation of the irrigation system needs to gradually complete the whole plot irrigation under the turn-on and turn-off valve sequence of several branch pipes in a rotation irrigation group. Correspondingly, there is a relatively high labor intensity in the operational mode, but with low investment cost, simple maintenance, and wide popularization, compared with the automatic operation of drip irrigation. In this study, a hybrid mathematical model was proposed to explore a better solution using the flow balance and structural constraints in the technical standards and division principles. Neighborhood characteristics were determined from the spatial distribution of branch pipes in the rotation irrigation group. The neighborhood search strategy and the repair of infeasible solution were given in the radius threshold range, further to find the critical path of the maximum extent. Four algorithms were selected to solve the model separately, including the genetic algorithm (GA), Greedy-GA, Tyson polygon-GA (Voronoi-GA), and Grid GA. The GA adopted the branch pipe model of actual number coding. The chromosome coding indicated that the branch pipe was opened in that group. The initial population was constructed using the Random, Greedy, Voronoi polygon, and Grid. The spatial distribution of branch pipe was applied in the neighborhood search strategy, further to serve as the structural feature in the rotation irrigation group. A uniform crossover strategy was adopted for the crossover and mutation of the offspring population. A competitive and elite retention strategy was adopted for the selection mechanism. Furthermore, the neighborhood structure represented the use of characteristic information. In the case of the rotation irrigation group, the spatial distribution of branch pipes presented critical structural characteristics. The specific search procedure was: first to establish the adjacency matrix of branch pipe, then to generate the reachable matrix using the Warshall, finally to calculate the critical path set for each valve, thereby converting the code. In a matrix form, the fundamental and non-key matrix was generated using a reachable matrix. Then the temporary path set was calculated and sorted to compare the valves in the non-critical matrix row for the standard deviation index. Feasible solutions were selected to update the matrix using four indicators from standard deviation, path length, connectivity, and running time. Furthermore, an optimal combination was achieved, where the mean flow rate in the rotation irrigation group was 260.06 m³/h, the standard deviation was 10.9 m³/h, the path length was 7 342.6 m, the mean path length was 8 105.2m, the mean path length within the group was 386.4 m, and the mean connectivity was 22. The neighborhood search strategy of the critical path was adopted to balance the distribution of branch pipes under the condition of objective function, suitable for daily management and maintenance. The Grid-GA and repair presented an excellent performance on the rotation operating group in a grip irrigation, particularly without the dimensional disaster in the multi-dimensional combination. Consequently, the hybrid optimal model and algorithm here can meet the hydraulic calculation and engineering requirements in modern agriculture. [ABSTRACT FROM AUTHOR]

This study aims to improve the low NOx conversion efficiency of the selective catalytic reduction (SCR) system caused by the soluble organic fraction (SOF) deposition of SCR carrier in a diesel engine. A sediment quantity model of SOF deposition was proposed to optimize key parameters using a multi-objective genetic algorithm (GA). Firstly, a road spectrum test at low temperatures was conducted to collect the data of SOF deposition amount in four groups. A SOF high-temperature pyrolysis was carried out to obtain 10 groups of experimental data at a steady-state temperature. Secondly, a model of SOF deposition was established using Matlab/Simulink tools, including SOF raw emission, the SCR carrier of SOF capture, and SOF pyrolysis module. Two ways were selected to calculate the SOF raw emission. One was the theoretical estimation using an approximate linear relationship of SOF with the gaseous unburned hydrocarbons (HC). Another was the direct measurement of SOF raw emission, where the excess air coefficient was used to correct the transient SOF raw emission. The capture efficiency of SOF by SCR carrier was evaluated via mapping the upstream discharge and exhaust gas flow of SCR. A correction was also introduced using the deposition amount of SOF. The SOF pyrolysis was prepared under the component analysis and chemical reaction kinetics model of SOF. Three stages were divided in a pyrolysis process of SOF, including the short, medium, and long chain. The key parameters of SOF pyrolysis were determined, such as the transient correction MAP, activation energy of three stages, pre-exponential factor, and mass proportion coefficient. Thirdly, various multi-objective GAs were evaluated prior to optimization. An interactive adaptive-weight GA (i-awGA) was selected to optimize the key parameters considering both efficiency and accuracy, whereas, a non-dominated sorting GA II (nsGA II) was used to identify the optimal solution, and a strength Pareto evolutionary algorithm (spEA) was utilized to generate the penalty function. Finally, a multi-objective GA optimization was performed on the transient correction MAP and three groups of pyrolysis parameters. In MAP optimization, the number, range, and distribution of MAP points acted by each gene were calculated using the type of MAP, combined with the basic value of MAP and the number of genes. In optimization of pyrolysis parameters, pre-exponential factors were calculated using the activation energy and compensation effect for the physical significance of the model. The average error of low temperature deposition of 4 groups of SOF reached 2.42%, the average error of high temperature pyrolysis of 12 groups reached 4.03%. Specifically, the largest average error of 3.04% was obtained for the low-temperature path deposition in one group of SOF, whereas, the largest average error of 5.41% was for the steady-temperature pyrolysis in two groups. It demonstrates that the proposed model of SOF deposition and the GA optimization was well suitable for the engineering application. [ABSTRACT FROM AUTHOR]

High-density dwarf culture has the characters of high yield and easy mechanization, thereby to be expected as a new development direction of fresh fruit industry. In harvesting, the mechanism of multi-arm cooperation can remarkably improve the operation efficiency of agricultural robots in the orchards of high-density dwarf culture. Generally, multiple picking zone are established for the collaboration of multi-arm harvesting robots, in order to assign proper picking objects to every manipulator. However, these different zones can be partially overlapped, leading to potential conflicts of manipulators in a robot. Path planning can be utilized to generate a trajectory leading the tip of manipulator to the goal without collisions. In this study, a global optimization using a Genetic Algorithm(GA) was proposed to solve the planning problem of harvesting tasks with overlapped picking domains in the multi-arm robots toward high-density dwarf orchards. 1) A multi-arm structure without dead zone in the fruit harvest was specially formulated as a sort of asynchronously multiple traveling salesman problem with overlapped zones. The following mild assumptions were made: a) The visual sensing system can precisely locate the fruits to be harvested; b) The fruits totally covered by leaves were not considered; c) The exceptional cases during the switching phases from one fruit to another were not considered in the planning stage, meaning that the traveling time linearly depended on the Euclidean distance. Five rules were established for the manipulators to work with better cooperative behaviors in a safe region of the workspace, where no collision occurred. 2)A modified GA was applied to optimize the collision-free trajectory planning of a flexible manipulator, further to ensure the operational safety without conflicts in the shortest traveling time. Gene codes were used to formulate various domain-manipulator pairs, thereby to determine picking sequences in a manipulator. The total traveling time of travelers was the objective to be optimized, where the population of gene codes was optimized iteratively in the proposed GA. In this case, the critical phases were coding and decoding. The double chromosomes were selected to formulate various picking sequences. After that, three operators were introduced, including selection, crossover, and mutation. The rule of synchronization was designed to avoid different travelers visiting the same cities simultaneously, in order to cope with the a synchronism in a queuing situation.3)Once the optimized solution was obtained, the planner can easily achieve the proper sequences for each manipulator to pick fruits via decoding the double chromosomes. As such, each manipulator performed the corresponding task, and the overall picking time was therefore reduced. The test results showedthat:1) The proposed planning converged at 500 and 2 000 iterations, when solving 43 fruits and 90 fruits planning problem with 4 manipulators.2) Compared with the sequential traverse, the modified GA increased the efficiency by 41% and 54.98%, when harvesting 43 fruits and 90 fruits, whereas, increased the efficiency by 4.25 times, compared with a single manipulator robot when harvesting 90 fruits.3) Compared with the sequential traverse and random traverse, the modified GA increased the efficiency by 110.69% and 27.18%, 20.45% and 23.33%, 12.94% and 21.69% under three different distributions, respectively. The contributions can be: a) A new planning strategy was proposed for the multi-arm harvester to avoid collisions, where each manipulator behaved cooperatively. b) A novel genetic algorithm was presented, where the coding and decoding was first proposed in this field. c) The rule of synchronization was first proposed to deal with the simultaneous visits in the queuing phenomenon during planning. d)A four-arm cooperative harvester was used to verify the effectiveness of the system, where the ergodic picking of each arm can be achieved without conflicts within a minimum duration. The framework can be generalized to many configurations of harvesters, suitable for robots with different number of arms, different varieties of fruit, and different ranges of overlapped domains. [ABSTRACT FROM AUTHOR]

The goal of this study was to obtain an optimal nutrient solution formula of cucumber for substrate cultivation based on the comprehensive evaluation of cucumber yield, quality and fertilizer use efficiency. The concentrations of nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg) were designed by a five-level (1/2) quadratic orthogonal rotation combination design method to obtain a total 23 treatments. The experiment was carried out in Yangling, Shaanxi of China (34°28′N, 108°07′E) from March 22 to July 10, 2018. The cucumber ‘Bonai 526’ was planted in substrate bags. The substrate had the organic matter content of 185.42 g/kg, the pH value of 7.28, and the electrical conductivity of 2.05 mS/cm. During the experiments, the cucumber yield was determined. The quality indexes including soluble protein, vitamin C and total soluble sugar content, organic acid and nitrate content were measured. The comprehensive evaluation score of cucumber yield and quality was calculated by a linear weighted model and then its relationship with concentrations of four elements was fitted by a quadratic regression model. The effect of two and three factors on the comprehensive score of cucumber yield and quality were analyzed. Subsequently, a multi-objective optimization model and a genetic algorithm were used to obtain the optimal nutrient solution formula. The results showed that the optimal treatments were different based on different yield and quality indexes, indicating that the comprehensive evaluation is necessary. Comprehensive evaluation scores of the 23 treatments were obtained and used for ranking. Different treatments of solution formula had significant effects on the comprehensive evaluation scores of cucumber yield and quality. A model was established to describe the relationship between of comprehensive evaluation scores of cucumber yield and quality and the coding levels of N, K, Ca and Mg (R2 =0.99, P<0.001). From the model, the coupling effects of four elements on comprehensive evaluation scores were explored and the results showed that the coupling effects of N and K, and N and Ca on the comprehensive evaluation scores of cucumber yield and quality was significant (P<0.05)．The interaction between N and Ca coupling had a positive effect, whilst the interaction between N and K coupling had a negative effect. Similarly, the element use efficiency models of the four elements were also established with the R2 values between 0.710 and 0.944. The multi-objective optimization model and genetic algorithm evaluation method suggested that the optimal combination of N, K, Ca and Mg concentrations in nutrient solution was 14.83, 6.89, 3.55 and 4.17 mmol/L, respectively. Under the optimal conditions, the comprehensive scores of optimal cucumber yield and quality were 38.52×10-3 while the comprehensive scores of element use efficiency of N, K and Ca were 55.09%, 63.77% and 30.55%, respectively. The optimal nutrient solution formula was better than the commonly used Japanese Yamazaki cucumber special formula. The cucumber yield per plant, soluble protein, vitamin C and total soluble sugar content of the former were 21.07%, 40.91%, 53.33% and 11.48% higher than those obtained by the latter, while the organic acid and nitrate content of the former were significantly lower than those of the latter by 4.67% and 16.78%, respectively. The findings provide an insightful guidance for high yield and quality of substrate-cultivated cucumber production, and further for the scientific management of nutrient solution in cucumber farming. [ABSTRACT FROM AUTHOR]

This study aims to propose a collaborative control strategy with the task assignment and whole area coverage for heterogeneous groups of agricultural robots under the complex environment of a farmland. The new system was also utilized to reasonably allocate the workload and work area of each robot in the collaborative operation of agricultural robot groups, and thereby to improve the overall work efficiency in precision agriculture. Various performance parameters were considered, including the energy consumption, failure rate, historical workload, and service quality of agricultural robots. Taking the overall efficiency of a robot team as the optimization goal, the agricultural robots were selected to effectively perform the specific task, where the workload of each participating robot was determined in the collaboration control framework. The complex working environment of agricultural robots was set, according to the actual characteristics in the digital ecological circular farm of the Shandong University of Technology and Zibo Hefeng Seed Company, China. A field experiment was carried out on the whole area coverage strategy of robot groups. The farmland that separated by the intertwining road was taken as a primary partitioning, according to the actual farm working environment. The primary partitions were rasterized for environmental modeling operations, and thereby the common working range of robot groups was set to the unit length of cells in the grid. Specifically, the irregular obstacle was treated by the expansion operation in binary morphology of image processing, when the edge line of obstacle cannot align to the edge line of raster in the simulation. In the modeling of rasterized environment and the treatment of obstacle expansion, the raster partitioning at the second level was established inside the first level partitioning. The merging operation between the raster partitions was also conducted to reduce the number of partitionings in the raster diagram. The operations expansion and reduction were carried out for the enlarged obstacles after the partition merging. The Genetic Algorithm(GA) and Hhybrid Particle Swarm Optimization (HPSO) were combined to improve the traditional GA crossover operation in computer-assisted support systems. The genetic diversity and chromosome population structure were utilized to enhance the convergence speed, where the chromosome was crossed with the chromosome of real-time optimal fitness value in iterative process. The GA concept of chromosome population diversity was established to consider the differences of fitness value for the chromosome in GA, and the species diversity in the different phases, thereby to set the adaptive crossover probability and mutation probability. The improved GA was then used to solve the problem of traversal sequence in a depth first search algorithm, when traversing over the partitions of the first and the second level. The path search rules were set in the second level partition, where the starting point and end point of a traversal were determined in the depth-first search algorithm. The work area of robot was allotted according to the workload of robot using the depth first search algorithm, when traversing in the raster chart. The traversal rules of robot in the working area were set by A* algorithm and eight-neighborhood search algorithm, further to realize the complete coverage of robot groups on the whole region. The simulation results showed that the path length of traversing each partition, iteration times, and the converge time to the optimal solution in the improved GA were 2.8%, 69.5%, and 64.2% less than those of the traditional GA, while, 9.3%, 19.0%, and 9.9% less than those of the simulated annealing algorithm. When the total area of obstacles remained unchanged, the total area repetition rate of robot population covering the whole area increased, as the number of obstacles increased. The total area repetition rate of obstacle with the highest dispersion in the map was 23.4% in the simulation experiment. Four types of special farmland were set, where there were different shapes but the same total amount of workload. 7 obstacles with the same positions, shapes, sizes and quantities were set in each special shaped farmland. The total area repetition rates of robot were 16.7%, 13.1%, 11.9%, and 6.7%, when traversing the four farmlands. The experimental results showed that the task assignment and regional coverage scheme can achieve the robot group at work full coverage of work area within the prescribed time. The work strategy of robot groups can provide a theoretical support for collaborative operation of agricultural robot groups on the complex environment in modern agriculture. [ABSTRACT FROM AUTHOR]

The contradiction between supply and demand of land resources has become increasingly prominent, as the rapid development of urbanization. This problem has hindered the improvement of urbanization and development quality. The optimization of land-use allocation can bean important approach to coordinate the limited land resources, and thereby to ensure the high-quality development of a city. This study aims to establish a spatial optimization model of land use via a multi-objective optimization model with NSGA-III. A multi-objective model consists of the main and the constraint objectives. The main objectives include the maximization of GDP value, the maximization of ESV, the minimization of changing cost from the status que, and the minimized incompatibility of land use types. Besides, the constraint objectives are comprised of 5 quantitative constraints and 4 spatial constraints dataset according to policy planning. The NSGA-III can be well used to solve the multi-objective space optimization of land use, due to its excellent ability of global optimization and spatial search. The recombination and mutation operator were improved, based specifically on the characteristics and developments of geographical units. The efficiency of modified model was improved remarkably via integrating the GPU parallel computing. The Dongxihu District of Wuhan, China, was taken as the study area to test the model. Two typical schemes, including ecological and economic priority, were analyzed to compare the time-consuming of model in the serial computing of CPU and parallel computing of GPU. Consequently, the results demonstrated that: 1) A better optimization efficiency of modified model can be obtained using the GPU parallel computing, where the computing time reduced from 158.08 hours to 1.68 hours. 2) The modified model can be used to coordinate multiple objectives, and thereby to reasonably optimizing land use in terms of quantity structure and spatial pattern, providing for the multiple selections indecision making. In the scheme of ecological priority, the ecological benefits of study area reduced by 6.16%, and the economic benefits increased by 13.64%. In the scheme of economic priority, the ecological benefits reduced by 6.19%, and the economic benefits increased by 15.86%. [ABSTRACT FROM AUTHOR]

Aiming at the current time-consuming and manpower-spending problems of prenatal behavior monitoring of ewes, a monitoring system based on the acquisition node of the collar was designed. The acquisition node of the self-made collar was integrated with the MPU6050 sensor, which collected prenatal behavioral acceleration information of ewes in real-time. The collected acceleration data was wirelessly transmitted to the embedded base station through Zigbee technology. The server received the data from the GPRS module in the base station, and then the data was stored in the MySQL database. Finally, the data was displayed on the webpage or mobile phones. According to the monitoring video of ewes, the collected acceleration data was calibrated at the same time, which provided sufficient samples for the behavior classification models. Besides, aiming at the problems of low accuracy and less recognizable behaviors of ewes in labor, regarding the collected acceleration data as the research object, a recognition method for prenatal behavior of ewes was proposed based on the classification model of the interval threshold and the Genetic Algorithm-Support Vector Machine (GA-SVM). In this method, three-dimensional acceleration data was firstly synthesized into one-dimensional resultant acceleration data, and then the resultant acceleration data was preprocessed by noise reduction of db5 wavelet and extraction contour of the sliding window. According to its amplitude fluctuation characteristics, the interval threshold classification method was used to recognize the two behaviors of walking and lying. In the light of the obvious characteristics of the spatial clustering effect for the three-axis acceleration data of drinking, eating, and ruminating behavior, the Genetic Algorithm (GA) was used to search the global optimal solution of penalty parameter and kernel function parameter of the Support Vector Machine (SVM) to accurately recognize the three behaviors. Finally, through calculating the four indicators including accuracy, precision, sensitivity, F1-score based on the confusion matrix, and then the performance between the classification method proposed in the research and several common classification algorithms were evaluated. The comparison results illustrated that the F1-score of the classification method proposed in the research was inversely proportional to the movement amplitude of ewes, and its precision was also inversely proportional to the sample size. The average accuracy of the classification method proposed in this work was 97.88%, which was 31.26%, 21.87%, and 21.9% higher than the traditional decision tree algorithm, the K-Nearest Neighbor (KNN) algorithm, and the SVM algorithm, respectively. The main reason was that with the increase of behavior recognition types, using only one feature vector and classification algorithm, it was difficult to find the most suitable partition hyperplane, resulting in the unavoidable decline in the correct recognition rate of certain behaviors. The experimental results showed that the monitoring system for the prenatal behavior of ewes based on the acquisition node of the collar could collect and transmit the activity information of prenatal ewes’ neck simultaneously. Besides, the classification method based on the interval threshold and GA-SVM could effectively distinguish five kinds of prenatal activities including drinking, eating, ruminating, walking, and lying. The number of identifiable behavioral types increased, and the applicability was greatly improved. This work also set up a control group to conduct a comparative experiment and found that the prenatal ewes had no abnormal behaviors after the 24 h adaptive period. Therefore, the acquisition node of the self-made collar would not affect the prenatal behaviors of ewes. The results were of great significance for establishing the amount of exercise and health assessment models of prenatal ewes, improving the efficiency of reproduction, and realizing intelligent management. [ABSTRACT FROM AUTHOR]

Colony plate counting methods as the national standard method are common and traditional for quantity inspection of living bacteria in food and agricultural products. The colonies are counted by manual counting and computer vision counting methods because of the color difference between colonies and background (such as medium and the edge of petri dish). However, colonies and background with similar color will interfere with the colony segmentation and lead to the deviation in counting results. Considering colonies and background have different spectral information resulting from different chemical compositions, hyperspectral imaging technology can be applied to colony segmentation and adherent colonies separation combined with chemostics. The segmentation method of colony and background, separated method of adherent colonies and calculation method of colony number were developed for colony counting. The hyperspectral images of Bacillus subtilis (B. subtilis) colony plate were acquired in the wavelengths from 431 to 963 nm. Spectral information of colonies and background (medium and the edge of petri dish) was extracted after preprocessing hyperspectral images. Genetic algorithms (GA) was used for processing spectral data and eleven characteristic wavelengths were selected (604, 636, 790, 799, 748, 683, 492, 437, 558, 470 and 928 nm). Genetic Algorithms least Square Support Vector Machine (GA-LS-SVM) model was established for distinguishing colonies and background by using the spectral information at the eleven characteristic wavelengths. The identification model with identification rate of 97.22% indicated that the colony and background could be successfully distinguished. The segmentation method of colony and background was developed. The spectral information of every pixel was extracted to identify whether it is colony or background by using the identification model. The binary image of colony segmentation was obtained through the spatial information in hyperspectral images. The location of colony was assigned as 1 and the location of background was assigned as 0, resulting in colony segmentation. The hyperspectral image at 604 nm was used for segmentation of adherent colonies to obtain binary image of adherent colonies segmentation. The segmentation threshold between background and colony was set as 0.5. The results demonstrated that the colonies were successfully segmented from background and adherent colonies could be accurately separated. Finally, the isolated colonies were counted by contour tracking algorithm after colony segmentation from background and adherent colonies separation. For application, acquisition of hyperspectral image, colony segmentation, adhesion colonies separation and colony number calculation were used for B. subtilis colony counting of real samples. The time required by the developed method was about 10 min. Its average relative error of colony count was 4.2% with the manual counting method as the standard method. In addition, the colony plate counting was performed by using computer vision counting method. The average relative error of colony counting was 8.3% which was higher than the developed method. These results indicated that this method performed better than computer vision counting method though the consuming time was longer than that spent by the automatic colony counter. This method with high accuracy can become a novel plate colony counting method and provide technical support for the detection of microbial quantity in food and agricultural products. [ABSTRACT FROM AUTHOR]

The objectives of this study were to estimate models for organic matter content of Shajiang black soil, which provided a basis and reference for rapid quantitative estimation of soil organic matter. In this study, Shajiang black soil in Shangshui county of Henan province was collected, the organic matter content and hyperspectral reflectance of Shajiang black soil were analyzed and tested simultaneously, and hyperspectral characteristics of Shajiang black soil were studied. Then, the original reflectance was converted to the first derivative spectral with Savitzky-Golay (SG) smoothing. Difference Spectral Indices (DSI), Normalized Spectral Indices (NSI), and Ratio Spectral Indices (RSI) of these two forms spectral were calculated from all available combinations with the reflectance of two random bands between 350 and 2 500 nm, and correlated to soil organic matter content, then the key spectral index and quantitative models for organic matter content of Shajiang black soil were developed. On the other hand, the sensitive bands of soil organic matter were extracted with the Genetic Algorithm (GA) and quantitative models of soil organic matter using Support Vector Machine (SVM) were established. The results showed that the spectral reflectance of Shajiang black soil under different organic matter levels had the same trend, and organic matter levels had a certain influence on reflectance. Organic matter content was higher, the reflectance would be lower, and on the contrary, the reflectance would be higher. The bands with good correlation between organic matter content and DSI, NSI, RSI based on the original reflectance were mainly concentrated near 650, 1 500 and 2 200 nm, DSI composed of reflectance of 995 and 1 911 nm, NSI composed of reflectance of 2 067 and 2 208 nm, RSI composed of reflectance of 1 037 and 1 908 nm had the better fitting degree. The bands with good correlation between organic matter content and DSI, NSI, RSI based on the first derivative spectral after SG smoothing were mainly concentrated in the combined band region of 1 350-2 000 and 600-1 000 nm, DSI composed of the first derivative of 792 and 1 420 nm, NSI composed of the first derivative of 792 and 1 389 nm, RSI composed of the first derivative of 792 and 1 389 nm had the better fitting degree. For all the spectral indices that were calculated, RSI composed of the first derivative of 792 nm and 1 389 nm gave a better prediction performance, the coefficient of determination was 0.81. Testing of the monitoring models within dependent data indicated that the coefficient of determination and root mean square error of validation were 0.91 and 1.56, respectively. In addition, the sensitive band ranges based on the original reflectance which selected by GA were 461-470, 611-620, 661-670, 741-750, 1 461-1 470, 1 891-1 900, 1 901-1 910, 2 011-2 020, 2 071-2 080 and 2 141-2 150 nm, and the sensitive band ranges based on the first derivative with SG smoothing which selected by GA were 521-530, 531-540, 671-680, 761-770, 771-780, 831-840, 1 431-1 440, 1 451-1 460, 1 871-1 880 and 1 881-1 890 nm for the same sample. The bands of the first derivative with SG smoothing mentioned above were used as input to SVR, and the quantitative model of soil organic matter performed the best. The coefficient of determination and root mean square error of modeling and validation was 0.95 and 0.91, 1.01, and 1.69, respectively. The compared quantitative model with Support Vector Machine, RSI composed of the first derivative of 792 and 1 389 nm had a little lower modeling accuracy, but it could meet the need for estimating the organic matter content of Shajiang black soil. It was concluded that both methods based on RSI composed of the first derivative of 792 and 1 389 nm and SVM can estimate organic matter content of Shajiang black soil accurately. [ABSTRACT FROM AUTHOR]

Middle Route of the South-to-North Water Transfer Project was constructed into a large-scale series canal system in a centralized automatic control mode for an operation management. There are the striking characteristics of long water transmission lines, and large scale of water transfer. Specifically, the height difference is 100 m from Danjiangkou Reservoir to Beijing. The water distribution can be achieved by adjusting the control gate using artesian water delivery, without any online regulation reservoir along the canal line. Therefore, a highly accurate adjustment is necessary for the flow regulation and control of the gate group during the operation and scheduling process, in order to realize the safe timely water delivery in an appropriate way. Many difficulties have arisen on the hydraulic control and dispatch of the main canal, due to numerous buildings along the main canal, while, the variations in water demand of each water diversion gate. Furthermore, a large hysteresis of hydraulic response usually occurred, due to the limitation from the propagation speed of the water wave. Accordingly, the change in the flow of any water diversion or control gate along the canal line can cause water level fluctuations within a certain channel range, showing a strong coupling effect. As such, the risk of ice jam can increase significantly, because of large hydraulic fluctuations during the freezing period, particularly on large-scale series canal systems. How to reduce hydraulic fluctuations through joint dispatching of gate groups can efficiently restrain the occurrence of ice jams during the freezing period in this case. In this study, taking the minimum deviation of water level as the objective function, an adjustment system was designed, including two control links, a conventional PI controller, and an optimization controller, while, combining with a genetic algorithm, a simulation model for the optimal dispatching of the sluice gate group was established suitable for the frozen period of the canal, considering the flow restriction between the canal pools, gate opening, and adjusting rate constraint. An optimization controller was comprehensively demonstrated, according to the verified modeling effect and the parameter sensitivity, based on the simulation experiment of control gates in the canal system between the ancient and the north Juma River currently. The simulation results show that the maximum deviation of water level can be significantly reduced under the simulated operating conditions, when adding an optimization controller in the system, compared with the only PI controller. Specifically, the maximum deviation of downstream water level decreased by nearly 36%, whereas, the recovery time of system was nearly 2.9 h ahead of time, indicating that the proposed model has a positive effect on suppressing the excessive hydraulic response, and stabilizing the water level during the freezing period. This will be beneficial to reduce the risk of ice jam. The reason was that the decrease in the deviation of the water level can be implemented via the flow control at the inlet and outlet of each channel pool, while better coordinating the need for storage adjustment between channels. The maximum opening of each gate can be reduced, but the adjustment amplitude of a single gate opening increased for some channels. The adjustment of gate opening degree can be feasible, due to the constraint of gate opening and adjustment rate. In the solving process of genetic algorithm, the specific value of random disturbance flow was recommended relatively small, due possibly to a negative effect on the optimization direction. In the constraint parameter of flow range, d, there was only a limited influence on the fluctuation range of water level, and the number of optimization iterations. [ABSTRACT FROM AUTHOR]

Green crop is generally used as the foreground in image segmentation of agricultural visual navigation system because of its obvious chromaticity difference from the basic image background. However, for the segmentation of maize stubble row, there are many backgrounds in the field harvested by combine harvester, such as naked land surface, maize residues, which has little color difference with the maize stubble row. To achieve accurate and quick segmentation of maize stubble row, an image segmentation approach of maize stubble row based on genetic algorithm and threshold filtering noise was proposed in this paper. Firstly, the RGB color space was selected to accomplish the segmentation, which is a basic color space and widely used in machine vision automatic guidance systems. Secondly, to reduce the difficulty of image segmentation, the region of interest (ROI) was selected by calculating the maximum of column gray value accumulation. Thirdly, the gray-scale image of the maize stubble row was obtained through the optimized gray-scale operator. Besides, the genetic algorithm was often used in global optimization, which was used to optimize the gray-scale operator. Specially, the maize stubble row image’s pixel was divided into 3 classes: land surface, residues in rows and stubble tip incision, and the 3 classes were selected as the sample to optimize the gray-scale operator by genetic algorithm. Then, the segmentation of single threshold method was used to segment the gray-scale image. Furthermore, the segmentation of single threshold method is widely used in image segmentation because of its high efficiency and convenience. Lastly, the morphological corrosion treatment (MCT) and threshold noise filtering algorithm (TNFA) were applied to guarantee the integrity of the maize stubble row region and the noise points on both sides of the maize stubble row removal. In order to verify the effect of the method proposed in this paper, 200 test images were captured from Zhuozhou Experimental Station of Conservation Tillage Research Center of Ministry of Agriculture in Baoding City, China in each October during 2014－2016. The acquisition was often on sunny day, aiming at collecting images under different natural lighting conditions, different positions in maize stubble row field. The results showed that the average relative object area error (ARAE) by our method was only 24.68%, while the AREA of the iteration method and the OTSU method were 90.67% and 86.42%, respectively. The average processing time of a test image based on this paper presented algorithm was 0.16s, which was much more time-consuming than the OTSU method (0.07s), while the processing time of our method was less than the iteration method (0.25s). Therefore, the method presented in this paper achieved better performance than other methods in maize stubble row segmentation, and was effective to segment the maize stubble row in the complicated backgrounds. The presented method can provide precise basis for detection of guidance line in maize straw covering field. [ABSTRACT FROM AUTHOR]

With the development of computer simulation technology, the model establishment of variable rate fertilization with EDEM, demonstrates effectively the microcosmic dynamics behavior of fertilizer particles which can’t be analyzed by experiments. The calibration of discrete element model parameters mainly includes experimental determination and indirect calibration. When method of particle board is used to measure the static friction coefficient between particles, particle bounce and collision are inevitable and accuracy is difficult to pursue. The method of indirect calibration, using try-and-error method or quadratic polynomial regression, isn’t appropriate for the discrete element model of variable rate fertilization with nonlinear as well as multiple parameter values to be calibrated. Aiming at the problem above, a calibration method based on relevance vector machine is proposed. The discrete element simulation process of variable rate fertilization is a nonlinear system regarding model parameters as input and uniformity of fertilizer blending as output (when a group of parameters are given, certain uniformity value of fertilizer blending can be gotten by fertilization simulation). Firstly, the model parameter influencing the fertilization outcome of the discrete element simulation most can be defined as the main parameters by sensitivity analysis. The value domain of each main parameters are found and then the sample of parameters are got. The sample of parameters and the corresponding uniformity of fertilizer blending are regarded as training and test sample. The relevance vector machine is used to reveal mapping relationship between model parameters and the uniformity, and the regression model is established. The uniformity based on the optimal model parameters should be consistent with the that in experiment, the model parameters fitness function is constructed combined the established models with experimental statistical results. Based on the mathematical thought of the constraint optimization, the mathematical model of optimal parameters calculating is established, and the optimal parameters are generated by the genetic algorithm. For A-type mixing cavity whose the collision edge is the outer convex curve, the mean relative error of uniformity between test values and simulation values from model calibrated: for nitrogen fertilizer is 6.4%, phosphate fertilizer of 4.1%, and potash fertilizer of 5.9%. While nitrogen fertilizer is 26.8%, phosphate fertilizer is 28.9% and potash fertilizer is 36.1% for the model before calibration. For B-type mixing cavity whose the collision edge is the straight-line, the mean relative error of uniformity from model calibrated: nitrogen fertilizer is 5.8%, phosphate fertilizer of 5.6% and potash fertilizer of 4.9%. While nitrogen fertilizer is 21.9%, phosphate fertilizer is 32.5% and potash fertilizer is 28.9% for the model before calibration. For C-type mixing cavity whose the collision edge is the concave curve, the mean relative error of uniformity from model calibrated: for nitrogen fertilizer is 5.0%, phosphate fertilizer of 3.7%, potash fertilizer of 8.7%. While nitrogen fertilizer is 36.2%, phosphate fertilizer is 31.6% and potash fertilizer is 24.4% for the model before calibration. The above results show that the method can be used to realize accurate calibration of discrete element model parameters of variable rate fertilization. [ABSTRACT FROM AUTHOR]

In order to study the attitude switching operation characteristics of the flexible chassis of agricultural vehicles, the attitude switching analysis of the flexible chassis and the attitude switching test based on the second-generation prototype running on the hard surface were carried out. The attitude switching state model was established, and the switching parameters were optimized by the analytic hierarchy process and genetic algorithm. The index weights were determined for the lateral running attitude and the situ rotation attitude, respectively. The first level indicator included the preparatory phase, the holding phase and the recovery phase in the attitude switching process. The secondary indicators included all test indicators such as preparatory precision, preparatory time, lateral precision, lateral time, rotation precision, rotation time, recovery precision and recovery time. The population size of genetic algorithm was 300 and the number of iterations was 200, the genetic operation was roulette selection method and single point intersection method, and the variation used the small probability variation. Through the study of the switching precision and switching time under different translation angle, motor speed, switching angle, translation speed and rotating speed were obtained, the influence of various factors and their interactions on the attitude switching of the flexible chassis and the matching relationship between different switching attitude parameters were obtained. The results showed that the primary and secondary factors affecting the preparatory and recovery precision were the translation anglemotor rotation speed. The translation speed of the lateral running attitude had a very significant influence on lateral precision and time. Under any translation angle, the optimal motor speed of the lateral running attitude was 5.4 r/min, and the optimal translation speed was 3.45 m/s. The primary and secondary factors affecting the rotating precision of the situ rotation attitude was switching angle>rotating speed. The primary and secondary factors affecting the rotating time of the situ rotation attitude was switching angle> rotating speed>interaction. Under any translation angle, the optimal motor speed in the situ rotation attitude was 5.4 r/min. When the switching angle β∈(0, 85°), the optimal rotating speed was (0.003 3β+0.506 8) rad/s. When the switching angle β≥85°, the optimal rotating speed was 0.78 rad/s. The optimal switching verification test results showed that the overall trend of the theoretical and experimental value of comprehensive precision and time was consistent. For the comprehensive precision, the maximum deviation was 1.00% and the minimum deviation was 0.75%. For the situ rotation attitude, the maximum deviation was 1.70% and the minimum deviation was 0.97%. For the comprehensive time, for the lateral running attitude, the maximum deviation was 698 ms and the minimum deviation was 454 ms. For the situ rotation attitude, the maximum deviation was 361 ms and the minimum deviation was 191 ms. The optimization parameter comparison results showed that, in the lateral running attitude, the test results of the optimized parameter group was equal to the precision priority group and 4.16% higher than that of the time priority group in terms of comprehensive precision, the test results of the optimized parameter group was equal to that of the time priority group and 17 110 ms less than that of the precision priority group in terms of comprehensive time. In the situ rotation attitude, the test results of the optimized parameter group was equal to that of the precision priority group and 5. 15% higher than that of the time priority group in terms of comprehensive precision, the test results of the optimized parameter group was 646 and 996 ms less than that of the time priority group and the precision priority group, respectively. The research shows that the optimized attitude switching parameter can ensure the flexible chassis complete attitude switching process with faster attitude switching efficiency with slight loss of attitude switching precision. [ABSTRACT FROM AUTHOR]

In order to achieve the fast and efficient detection of water-holding capacity in meat products, this paper developed a rapid method to detect water-holding capacity in beef using color sensor and genetic algorithm. First of all, the beef was cut into samples with size of 4 cm × 1 cm × 0.5 cm. Then test paper, decorated by cobalt chloride, could change its color when it attached sample. The color can establish a certain relationship to water-holding capacity of the sample. However, it is not easy to judge the changed color of test paper by naked eye. Therefore, color sensor was applied to convert the detected color’s spectrum into specific color parameters, which was manipulated by Arduino controller. Pressure method, as a traditional way to measure water-holding capacity in meat products, is precise but it costs more time and wastes more material. To establish a neural network prediction model of water-holding capacity, this paper set the color parameters as the input vector and set the water-holding capacity measured by the pressure method as the output value. Experimental data derived from 80 samples, which included 60 training samples and 20 testing samples. The back propagation (BP) neural network was trained by 60 samples. Besides, the accuracy of BP neural network was verified by 20 samples. However, the BP neural network had the deficiencies of insufficient network global research ability, slow convergence and local optimum iteration. The genetic algorithm optimized the weights and thresholds in BP neural network, and thus enhanced the accuracy of prediction. The fitness function of genetic algorithm was the sum of square error. After 100 iterations, the best fitness function was obtained. The weights and thresholds optimized by the genetic algorithm were put into the BP neural network, the BP neural network was retrained and the accuracy was tested. The result showed that the optimum attachment time of test paper was 20 s. The optimized BP neural network model based on genetic algorithm had a better ability for nonlinear approach. The determination coefficient of the regression line is 0.987, and the slope of the best linear regression equation is 0.96. This showed that the deviation between the predicted value and the actual measurement value of the BP neural network optimized by the genetic algorithm is very small. Thus the optimization of the model is successful. The prediction accuracy of the BP neural network model was improved from 90% to 95% after being optimized by genetic algorithm. Compared with the pressure method, using color sensor not only greatly shortened the detection time, but also reduced the waste of resources in the detection process. What was more, the cost of color sensor method was lower than NIR (near infrared) spectroscopy method. The predicted results from the optimized BP neural network based on genetic algorithm were better than BP neural network. This detection method is fast and accurate and has low cost. The results provide a reference for further development of intelligent detection equipment for meat products. [ABSTRACT FROM AUTHOR]

Most orchard gardens are distributed in the subtropical and tropic zone, and especially in mountainous and hilly region in China. With the development of science and technology and the increasing cost in labor, the mechanized operation for fruit collection is needed. Existing small fruit collecting devices have bulky and complex structure and need to be equipped with high-power drive, and strict requirement in the tree spacing. In order to adapt to the mechanization collection of small fruit and improve the mechanical collecting device, based on the existing fruit collection equipment at home and abroad, and considering deployable and foldable mechanisms of the equipment, a collecting device with flank deployable and foldable mechanism was introduced in this paper. This collection device mechanism can reduce the bulk in process of moving, adapt in line spacing of the fruit trees, have larger coverage area in unfolding state, and can adjust quickly and effectively in operation to improve collecting efficiency. The collecting device consisted of three parts: flank deployable and foldable mechanism (the core mechanism), lifting mechanism, and mobile clamping mechanism. The displacement and coordinate point G at the end of the flank deployable and foldable mechanism were calculated by transformation matrix. Then kinematics of the flank deployable and foldable mechanism was analyzed, and the main components of the mechanism were optimized using genetic algorithm (GA) operated in MATLAB. Rod AB, AD, PE were taken as the main optimization variables by comparing the influence of variable rod length on the displacement of point G and ∠EFG. Given the radius in folding and unfolding, the maximum displacement of point G during the unfolding process and the minimum dimension sum of the rods were taken as the optimization objectives. In order to obtain the more preferable rod dimension, the optimization range of the rods was expanded about ±50 mm. And the final optimization range and optimal solution of the main rods dimension were obtained based on the restrictions by iterative calculation at last. The optimal dimensions of the main components were lAB=277.88 mm，lAD=661.64 mm, lPE=306.58 mm. The obtained results were used to modify the 3D model of the flank deployable and foldable mechanism, and the 3D model of this mechanism was imported into the dynamic analysis software ADAMS. From such model, then the motion trajectory of the mechanism was simulated and the dynamic simulation results showed that the driving torque was 2.4 N·m. The motor type was determined based on the results achieved by simulation. Finally, a prototype of the flank deployable and foldable mechanism was manufactured and tested based on the optimal theoretical model, and the trajectory of point G was recorded by the Phantom v5.1 high-speed camera. Due to precision and errors of the prototype caused by machining and assembling and errors from camera shooting angle and image processing, there was a slight deviation between trajectory of experiment and virtual simulation, but they were basically consistent, which verified accuracy and reliability on the theoretical design of this collecting device. Experiment results of mechanical harvest using a small fruit collecting device with flank deployable and foldable mechanism demonstrated that this collection device had practical value and this research provides a reference for the improvement of the other collection mechanism. [ABSTRACT FROM AUTHOR]

With the rapid development of modern agricultural technology, plant factory has become the most advanced development stage of facility agricultural. At present, the majority of work tasks in plant factory completed by manpower are labor-intensive and low efficient, therefore, the agricultural intelligent equipment system has become a hot spot in the development of plant factory. In view of the task demand of the carrying and spraying of the three-dimesional seedling tray, the three-dimesional seedling tray management robot was developed. In order to make the manipulator of three-dimesional seedling tray management robot complete all carrying and spraying tasks flexibly and efficiently, meanwhile to reduce operating space and structure size of manipulator, parameters of the manipulator were optimized by the method of theory and experiment. Firstly, in order to determine the relationship between the end coordinate of the manipulator’s connecting rod and the base coordinate system, the kinematic model of the robot system was established by D-H method, which was important theoretical basis for the workspace analysis. Then the workspace of manipulator was constructed by graphic method, and the workspace constraint conditions were determined according to the condition that manipulator workspace accommodated target workspace. Based on that, the objective function was established according to shortest distance and compact structure, and genetic algorithm was used to solve the objective function. The optimal rod lengths (big arm, medium arm, small arm) of the manipulator were 648, 472, and 396 mm, and the limit values of the optimal joint angle were 96○, 68○, and 126○. The workspace and the target workspace of the robot were depicted in the MATLAB (Matrix Laboratory) software platform according to the optimal solution of the manipulator parameters, the kinematics equation of the robot and the range of the manipulator’s parameters. The simulation result showed that the target workspace was between the inner limiting envelope interfaceand the outer limiting envelope interface of the manipulator, which verified the manipulator’s ability to cover the target workspace, and the rationality of the theoretical optimization for the parameters of the manipulator was proved. Finally, in order to further validate whether the manipulator could complete all the action tasks of the target workspace, the robot prototype and the three-dimesional seedling tray experimental platform were built in the laboratory, and the motion planning test of carrying and spraying of the robot system prototype was carried out. The carrying test was planned as follows: According to the target workspace size and the theoretical position coordinate value, the manipulator was controlled to move vertically upward from the lowermost (lower limit) to the topmost (upper limit) of the target workspace, this group of actions were repeated 100 times, and seedling tray was always placed horizontally during carrying. The carrying test mainly verified the manipulator’s ability to cover the target workspace in the vertical direction. Spraying test steps were as follows: 1) The initial spraying height value was 100 mm; 2) Divide the seedling disk plane into m×n grids, and each grid point represented the spray position point, m=10, n=20; 3) The target path point group consisted of all the spray points at the current height, and the manipulator was controlled to pass through the target path point group sequentially; 4) The spraying height value was increased by 20 mm; 5) Repeat step 2), 3) and 4) until the spraying height value was equal to 1 020 mm. The spraying test mainly verified the manipulator’s ability to cover the target workspace in the horizontal direction. The high-speed video camera system was used to mark trajectory coordinates of manipulator in the motion planning test of carrying and spraying (high-speed camera was KODAK’s color CCD (charge coupled devices) camera, a resolution of 512×480 pixels, frame rate of 125 frames/s). Test results showed that the optimized manipulator could reach all limiting positions and other characteristic positions of target workspace, and the maximum relative positioning error was 0.98% which was within error range and could meet the accuracy requirements for manipulator containing the target workspace effectively; what was more, it was proved that the optimal parameters of manipulator were reasonable. Parameters optimization and experiment of three-dimesional seedling tray management robot could provide the reference for trajectory planning and motion control. [ABSTRACT FROM AUTHOR]

Accurate prediction of winter wheat chlorophyll content at seedling stage is important for guiding precision management in the field. In order to acquire chlorophyll content of winter wheat leaves, traditional detection methods require to squash winter wheat leaves and applying chemical methods, which would have bad influence on crops growth and cause unnecessary waste of time on some level. It is proved that the spectroscopy analysis is an effective method to predict chlorophyll content of winter wheat. However, the drift and offset of spectral baseline has a great influence on the predicting accuracy. So, this study was carried out to eliminate the influence of the drift and offset. The experimental farm was randomly divided into 70 different sampling areas in Xiaotangshan, Beijing, and the winter wheat leaves were collected on April 20th in the period of seedling stage. The visible and near infrared canopy spectral reflectance of winter wheat was measured by an ASD FieldSpec handheld spectroradiometer at seedling stage. The chlorophyll contents of sampling leaves were detected by the spectrophotometer in the laboratory on the same day. The obtained data of the canopy spectral reflectance and chlorophyll content were assembled for each region individually. The multiple scattering correction (MSC) was used on the bands of 325-1025 nm wavelength, because many scattering errors were introduced into the measured spectral data due to the physical factors. The MSC method first requires establishing an ideal spectrum of all samples, and modifying all the other samples of near infrared spectra on the basis of ideal spectrum to, and spectral reflectance changes with the content of chlorophyll components in the sample meet the direct linear relationship. The absolute intensity difference of spectral reflectance of winter wheat canopy was weakened after the MSC pretreatment, and then scattering effect was effectively reduced. Baseline shift and offset problems were resolved, and the correlation coefficients of spectral reflectance and chlorophyll content were increasing through the MSC pretreatment. Furthermore, genetic algorithm (GA) was proposed for sensitive band selection. GA is a high efficient and globally random search optimization method which simulates Darwin's evolution by natural selection and genetic mechanism of biological evolution. According to the principles of choosing high frequency bands as characteristic wavelength, 486, 599, 699 and 762 nm crop canopy reflectances were selected to calculate vegetation indices, including ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). The correlation between each vegetation index and chlorophyll content of winter wheat was analyzed. It was found that the correlation between each vegetation index and chlorophyll content of winter wheat significantly increased after the MSC. The results showed that DVI and SAVI could refrain interference of soil background during seedling period, and the optimal parameters were DVI(762, 599), SAVI(762, 599), DVI(762, 699) and SAVI(762, 699), and the correlation coefficients were all above 0.6. The DVI(762, 699) and SAVI(762, 599) were selected to establish the multiple linear regression (MLR) prediction model and the least squares-support vector regression (LS-SVR) prediction model. The 70 winter wheat samples were divided into 2 groups, 50 samples for model calibration and the other 20 samples for model verification. The results of MLR showed the determination coefficient of the calibration model was 0.528 and that of the validation model was 0.487. In order to improve the precision of the forecast model, the LS-SVR prediction model was applied, and the determination coefficient of the calibration model was 0.681 and that of the validation model was 0.611. It showed that the fitting result was ideal. With the application of spectral technology, it provides a feasible method to detect the winter wheat growth status at seedling stage. [ABSTRACT FROM AUTHOR]

Fluctuations in aquatic product prices have an important impact on the development of the aquaculture industry. Accurate forecasting results can enable farmers to keep abreast of changes in the market and rationally plan aquaculture. Based on the non-linear, non-stationary and periodicity of the aquatic product price series, a genetic algorithm (GA) support vector regression (SVR) model based on time series for forecasting aquatic product price was presented in this paper. Firstly, the time series method was applied to the price series, the autocorrelation function was used to judge the stability, and the partial correlation coefficient was used to judge the data items, then the training data set was obtained. After that, the genetic algorithm was used to optimize the parameters of support vector regression. The parameters of SVR based on radial basis kernel function were kernel function coefficient, penalty factor, and loss parameter. We designed these three parameters by using real number coding individual representation. We used the selection operation to select the mean square error as the fitness function, to calculate the fitness value of each individual, and to select the individuals with better fitness value. By use of the crossover operation, we selected the point intersecting as crossover operator with different individuals, respectively in a corresponding position to a certain probability. The nature of mutation operation was used to enhance the local search algorithm, and avoid falling into the local minimum. We mutated individual to a certain probability and change its current value, then generated new population. We introduced the mechanism of 5-fold cross validation to the process of each iteration to obtain the optimized parameter combination. Finally, the support vector regression model was established by using the optimized parameters to forecast the price of aquatic products in the next period. In this paper, we selected mandarin fish, metapenaeus ensis and portunus trituberculatus as the experimental objects. The experimental data we used were the value of aquatic product price from January 2011 to December 2015 of Beijing Xinfadi market website (http://www.xinfadi.com.cn). After craw ling the web data-including 1,541 records of mandarin fish, 1,525 records of metapenaeus ensis and 1,430 records of portunus trituberculatus, we calculated the monthly average price to represent the price of a period. We trained the proposed model by using data from 2011 to 2014, and forecasted the price of the next year. Through comparing with the real value, the mean absolute percent error of mandarin fish, metapenaeus ensis and portunus trituberculatus was 6.70%, 7.82% and 14.76%, with corresponding root mean square error of 5.8531, 23.7011 and 13.8580, respectively. After surveying the market, we found that the results of forecasting were more in line with the actual situation. In this paper, the SVR model and the BP neural network model based on time series were all used in contrast experiment of our model. The experiment results showed that our model was superior. According to the characteristics of aquatic product price in this paper, we proposed a combined model for the determination of the relevant items of the aquatic product price series, the selection of the kernel function and the parameter optimization. The results showed that the proposed model can provide the basis for the forecasting of aquatic product price. [ABSTRACT FROM AUTHOR]

The processing technology of the machine picking seed cotton (MPSC) should be influenced significantly by the kinds and the contents of impurities. But during the MPSC processes in China, there is a lot of the mixed level and the mixed ginned, as well as the lack of online detection technology. In view of the present situations, a method for the identification of impurities in MPSC image using genetic algorithm (GA) to optimize the SVM parameters has been presented in this paper. First, in order to label three categories of the cotton fiber, the light-colored impurities and the dark impurities, the feature vectors were constructed for the image segmentation by the color characteristics of hue, saturation and intensity of pixel neighborhood, and the texture features of the average brightness, the average contrast, the smoothness, the third moment, the consistency and the entropy. GA of optimal retention strategy was used to optimize the penalty parameter and kernel function parameter to establish the SVM classifier of image segmentation, and then morphological operation such as hole filling, opening operation, closing operation was adopted. The specific operation was the use of open operation on light-colored impurities to eliminate the influence of small area noise; while the use of closed operation on dark impurities to smooth the target boundary on the basis of retaining small areas of dust. Then, for the impurity recognition process, shape features including area, perimeter, eccentricity, rectangle degree and shape factor were added to feature vectors besides color feature and texture feature of marked region; five categories of cottonseed, cotton leaf, bell shell, stiff valve and dust miscellaneous were marked; and the SVM classifier for impurity recognition by GA was established. At last, automatic segmentation and impurity recognition for MPSC image were realized using these two SVM classifiers. For the experiment MPSC was taken as the test material. The color planar array CCD camera of technical grade (MV-EM510C/M, Microvision, Inc.) with the industrial lens (M0824-MPW2) was used for the shooting system which included the LED diffuse light source of a 4-segment strip (AFT-WL21244-22W) and the light source controller (AFT-ALP2430-02). When shooting, the camera and the light source were placed in the darkroom, and the MPSC used for the test was pressed against the transparent glass plate on the other side of the darkroom. The data training and the picture test were conducted using MATLAB R2014b and libsvm-3.21 toolbox, and 60 pictures were equally used for both training and testing. The SVM multi-classifier was established during segmentation and recognition operation instead of simple binary classifier. This method utilized SVM small sample and high dimensionality learning ability, and the segmentation and recognition accuracy were further increased. Experimental results show that, comparing with the segmentation results from fuzzy C - means clustering algorithm and traditional SVM algorithm, the classification accuracy was improved significantly by using neighborhood spatial information. When the contrast of the edge of the target area in the image was weak, the suggested method could effectively avoid the situation of taking the shadow of the target as the edge which had shown great adaptability in applications. In this study, compared with the traditional SVM recognition algorithm, GA was used to automatically optimize SVM penalty parameter and kernel function parameter. SVM classifiers for image segmentation and impurity recognition were obtained through the suggested method overcame the blindness of parameter selection and the shortcomings of BP neural network generalization ability. Since the data distribution shapes of the homogeneous image in the color space are usually of similarity, and the characteristics of MPSC impurities texture are of stability, the optimal SVM parameters are common to the same kind of ima [ABSTRACT FROM AUTHOR]

When a large number of fertilizers were applied, deep fertilization was performed to avoid damaging seeds and causing environmental pollution in China. But when deep fertilization was used, it was difficult to control sowing depth which would influence the sowing quality and reduce the crop yield, so the sliding push opener was designed. The sliding push opener consisted of 2 parts: the shovel handle and the sliding pusher. The sliding pusher mainly relied on its surface to work. The surface of the sliding pusher consisted of a curve, a top side and a bottom side. The top side was wider than the bottom side and the vertical height between them was set to be 150 mm. The sliding push opener was an obtuse opener. Shovel handle was a 30 mm × 50 mm steel tube through which seeds could be delivered. As the sliding push opener's top side was wider than the bottom one, it was easy to push disturbed soil back into the furrow and control the sowing depth. In order to get the optimal structural parameters of the sliding push opener, the single factor tests, the response surface tests and the verifying tests were carried out. When the sliding push angle was 45°, the single factor tests were carried out for the top width (ranging from 30 to 78 mm) and 5 levels were set; when the top width was 33 mm, the single factor tests were carried out for the sliding push angle (ranging from 45° to 65°) and 5 levels were set. In the response surface tests, the testing factors were top width (ranging from 42 to 66 mm) and sliding push angle (ranging from 35° to 55°), and 3 levels were set for each factor. The response surface testing scheme designed with the central composite face-centered design (CCF) method was a two-factor three-level testing scheme. The response surface analysis method was used to analyze the testing data and optimize the working parameters of the sliding push opener. The Design-expert 8.0.5b software was used to analyze the test data, and then the credible regression mathematical models were obtained; after that the genetic algorithm NSGA-II of multi-objective optimization was used to optimize the regression mathematical models. The results indicated that the order for primary and secondary factors affecting soil returning quantity was: sliding push angle > top width, and that affecting forward resistance was: sliding push angle > top width. The optimal structure parameters were obtained: the bottom width was 30 mm, the height difference of the top and bottom part of sliding push was 150 mm, the top width was 54 mm and the sliding angle was 48°. Under the condition, back soil returning quantity was 8.67 g/cm and the forward resistance was 925.56 N. On the basis of the results of the verifying tests, it was found that the average soil returning quantity was 7.6 g/cm, and the average forward resistance was 935.78 N. The comparison between the optimization results and the verifying results showed that the relative error of soil returning quantity was 14.8% and that of forward force was 1.1%, which meant the optimization results were reliable. The design of the sliding push opener is very important for the consistency of sowing depth. The results of this research can provide new ideas and reference for the design and application of openers. [ABSTRACT FROM AUTHOR]

It is critical to design a reasonable and suitable mechanism parameter for the robot, as it plays an important role in the robot’s adaptability to the environments. To efficiently design a robot that was able to adapt itself to an environment where the obstacles were given, a mechanism-parameter designing method was hereby put forward based on the goal programming in order to obtain the optimized robot that could adapt to the environment mentioned. According to configuration evolution and configuration characteristics of tracked robot, a transformable tracked robot configuration was proposed, which only need 3 motors and its single arm could be used as the tracked robot with 2 arms. In theory, applying the ellipse theorem to the configuration design of the tracked robot could ensure that the tracks could be continuously tensioned with the track length unchanged, when the arm was swing. The continuous tension of track was realized by using cam and spring structure. On the basis of the above, the stair climbing and the gully crossing mechanism of the tracked robot was analyzed by using the swing arm's front and back swing. And then, the relationship between the structure parameters of the robot and the stair and gully obstacles was obtained, and the function of the obstacle performance was built. According to the requirements of the goal programming, we selected the distance between driving wheel and the driven wheel, maximum length of the swing arm, driving wheel radius and angle between the track and the horizontal plane in the process of obstacle surmount as the decision variables. Then the values of the highest stair and the widest gully were used to the performance function, and the objective function and object constraint of the goal programming were obtained. As the robot must meet some conditions in the obstacle surmounting process and must improve the calculation efficiency, the initial constraint and no-slip constraint in obstacle surmounting, and the upper and lower boundaries of decision variables were defined, and then the absolute constraints were obtained. Finally, the goal programming model of the structural parameters for the transformable tracked robot was established. Due to the limitation and low efficiency of the traditional algorithm for solving the goal programming model, this paper used genetic algorithm. Penalty function method was used to deal with the individuals that did not meet the absolute constraints to reduce their fitness to reduce the possibility of gene transfer to the next generation. In order to prevent the genetic algorithm converging the local optimal solution, the program ran many times, and the average value was taken from the 10 results, and then the structure parameters were as follows: the distance between driving wheel and the driven wheel was 290 mm, the maximum length of the swing arm was 326 mm, the driving wheel radius was 60 mm. To verify the feasibility of the obtained structural parameters, the track model was obtained by using the software Adams’ development function to replace the flexible track with small rigid body. Then the dynamic platform of the tracked robot was built and the simulation experiments of climbing 160 mm stair and crossing 300 mm gully were carried out, which verified the adaptability of climbing the stair and crossing the gully and obtained the driving torque required in the obstacle surmounting process. In order to further verify the rationality of the structural parameters obtained from the goal programming model, a prototype was developed and tested on the stair and gully. The prototype succeeded in surmounting 160 mm high stair and 300 mm wide gully, which proved the performance of the robot and also the feasibility of the structure parameter designing method based on goal-programming. This study provides an effective method for robot structure parameter designing. [ABSTRACT FROM AUTHOR]

System allowable pressure beforehand distribution between laterals and branch pipes, and the location analysis of the maximum and minimum water pressure points are often involved in the traditional optimization way of irrigation network for micro-irrigation. However, the study of maximum control area of basic irrigation controlling area network is inadequate. In this paper, in order to expand the control area, simplify the structure and lower the cost of basic irrigation controlling area network system, the objective functions of basic irrigation controlling area network were established. The function was modeled by taking some typical commercial pipes for branch and lateral pipe as the research object. The network’s laterals were unidirectional or bidirectional for micro-irrigation. There were two models including maximum control area model and the minimum cost per area model, respectively. According to the actual engineering requirements and taking practical types of pipes in the market into consideration, we selected seven pipes. These pipes had different types of diameter as branch pipes and same type lateral pipes. The branch pipes were connected together in the order of diameter from large pipe diameter to the small one. In addition, same diameter lateral pipes were connected to the outlets of branch pipes. Considering planting characteristics and the pattern of water requirements of crop, distance between two adjacent outlets of branch pipes or lateral pipes could be set as the constant standard length. In order to facilitate the optimal design of the micro-irrigation network, every type of pipes, including branch pipes or lateral pipes, was considered as being composed of integral units of constant standard length and a unique allowance (less than the constant standard length) that was located in front of branch pipes or lateral pipes. There were two lateral layouts in the system, unidirectional and bidirectional. Then pipeline network arrangement of basic irrigation controlling area was accomplished. First, we supposed that flow of every lateral outlet was in the condition of the design flow and maximum pressure drop of all outlets in the basic irrigation controlling area network system for micro-irrigation should be less than allowable pressure; then the optimal combinations of pipelines were given based on matlab genetic algorithm toolbox (GA toolbox). The pipelines met the pressure condition in accordance with irrigation uniformity. The network was considered as a whole in this method. Practically, the paper simulated water pressure factors such as the surface slope, and the hydraulic gradient for value of every outlet including all laterals and branch pipes. In the method, the system allowable pressure, and the location discussion of the maximum and minimum water pressure points could be ignored. Pipe diameter adjusting based on the optimal combinations result was not necessary. Nevertheless, the optimal combinations result could be applied to engineering design directly. The results showed that based on maximum control area model，the control area of the network with bidirectional laterals was significantly higher than unidirectional one. The number was increased by about 23%-24% and the cost per area was reduced by about 23%-36%；In the model of minimum cost per hectare, compared with unidirectional laterals, the control area of bidirectional was improved by about 105%-200%. Meanwhile, the cost was decreased by about 7%-10%. The optimization results indicated that the network with bidirectional laterals was superior to unidirectional one in all respects. Optimization design results can be improved continuously by adjusting system parameters such as pipe diameter and length. This design method has practical meaning and theory value in optimization of micro-irrigation network. [ABSTRACT FROM AUTHOR]

With the vigorous promotion of the national strategy for hydraulic engineering, a demand for mixed-flow pump in infrastructure has been increasing for years. As a kind of high-performance pump, the high specific speed mixed-flow pump has the advantages of stable performance and wide application area. With the increasing of specific speed, the high specific speed mixed-flow pump has begun to replace the axial-flow pumps in some application areas in recent years. It is necessary to optimize the impeller performance for it affects the pump performance directly. The velocity torque distribution along mixed-flow pump impeller is a significant parameter, which plays an important role in energy conversion between impellers and fluid. In the design of dualistic theory of mixed-flow pump impeller, the regulation of velocity torque distribution should be defined. But, there is no uniform method of expression as well as specific rule for designers to follow, and too much experience is needed to rely on. It has far-reaching meaning for the promotion of the level of mixed-flow pump design and the performance by establishing the optimization parameter model of velocity torque distribution along impeller. In order to further research the hydraulic performance of optimization method for high specific speed mixed-flow pump, a mixed-flow pump whose specific speed is 803 was chosen as the research object, and the commercial software CFX and the shear stress transport turbulence model were applied to compute the interior flow field within the pump. In this paper, the efficiency and the head were chosen as the optimization objectives, and 3 parameters that describe the velocity torque distribution were chosen as the optimization parameters, which were used to parameterize the impellers. The uniform design was adopted to arrange the sample space, the RBF (radial basis function) neural network was used to fit the relationship between the variables and objectives, and finally the NSGA-Ⅱ genetic algorithm was used for multi-objective optimization. Moreover, the difference of the internal flow field was obtained by comparing the initial linear distribution individual with the optimal efficiency individual and the optimal head individual respectively, which were selected from the Pareto solutions. The velocity torque distribution and the change trends of the wrap angel along the axial plane streamline between the initial and optimal individual were analyzed. The wrap angel values of optimal efficiency and optimal head were 75.15° and 67.85°, respectively. The variation trends of the wrap angel of micro-element based on optimal efficiency individual were contrary with the initial one, while the variation trends based on optimal head individual were the same as the initial one, but the change range was enlarged. Compared with the initial linear distribution individual, the efficiency of the optimal efficiency individual was improved by 1.12% through experimental verification. Distribution maps of the relative streamline, velocity and static pressure of the impeller guide vane at 0.5 times blade height and the static pressure of the corresponding chord length at 0.2, 0.5 and 0.8 times blade height were showed. And, the possible reasons for the differences between initial individual and optimal individuals were given. It was found that, utilizing the RBF neural network combined with NSGA-Ⅱ genetic algorithm, the effect of optimizing the impeller hydraulic performance of high specific speed mixed-flow pump was remarkable. The research provides a certain theoretical reference for further improvement of the performance of high specific speed mixed-flow pump. [ABSTRACT FROM AUTHOR]