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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid acquisition of soil water content (SWC) in field crop root zone is significant for drought supervision and precision irrigation. The UAV multispectral remote sensing system has the advantages of obtaining high spatial-temporal resolution of crop phenotype data, and has a wide application prospect in soil moisture monitoring. In order to obtain SWC accurately and timely at a farm scale, in this paper, the field maize with different water treatments is taken as the research object, and the multispectral remote sensing monitoring of summer maize is carried out by using the UAV remote sensing platform, and the soil water content of different soil depth in maize root zone is collected synchronously. Based on the UAV multispectral remote sensing image data sets of jointing stage, tasseling-silking stage and milky-maturity stage of summer maize in 2018, the soil background is removed by support vector machine classification, the spectral reflection of maize canopy is extracted, and the 10 vegetation indices are calculated, then the sensitivity analysis of soil water content in different depth is carried out by using full subset screening method for different wave bands and vegetation indices, and the soil water content in different depth is analyzed respectively, ridge regression and extreme learning machine are used to construct quantitative estimation models of soil water content at 0-20, 20-45 and 45-60 cm soil depth after full subset selection. The test area is located in Zhaojun Town, Dalate Banner, Ordos, Inner Mongolia, China(40°26'0.29" N, 109°36'25.99" E, elevation 1 010 m). The sowing time of maize is on May 11, 2018, the emergence time is on May 18, and the harvest time is on September 8, 2018. The total growth period is 114 days. The UAV multispectral remote sensing images and ground data collection dates are July 8, July 12, July 17, July 21, July 26, August 2, August 28 and September 7, 2018. It is collected once a day and tested 8 times in the whole growth period. July 8-21 is the jointing stage, July 26-August 2 is the tasseling-silking stage, August 28-september 7 is the milk-maturity stage. The flight altitude of the UAV is 70 m, and the flight time is 11:00-13:00 local time (11:44-13:44 Beijing time). Firstly, the UAV multispectral canopy images of field maize with 5 different irrigation treatments (TRTs) are acquired through the six-rotor UAV equipped with a RedEdge multispectral camera ( MicaSense, USA), and the multispectral images of diffuse reflector (reflectivity 58%, size 3×3 m) are collected at the same height to perform radiometric correction in the meantime, and then the spectral reflectances of the field maize are acquired. Secondly, the support vector machine (SVM) is used to eliminate the multispectral image of soil background in ENVI and ArcGIS software, then the maize canopy spectral reflectance is extracted and 10 vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), Normalized Green Difference Vegetation Index (GNDVI) and Transformed Chlorophyll Absorption In Reflectance Index(TCARI), etc, are calculated. Finally, the full subset selection method based on Bayesian Information Criterion (BIC) is used to analyze the sensitivity of SWC at different depths for different spectra bands and vegetation indices in R3.5.1 software, and then Ridge Regression (RR) as well as Extreme Learning Machine (ELM) are used to construct a quantitative estimation model of SWC at soil depths of 0-20, 20-45 and 45-60 cm at different growth stages, respectively. The results show that the full subset selection method based on BIC can effectively select the optimal spectral subset, and the selected variables generally pass the significance test and the independent variables number is small; the effects of the ELM model outperformed the RR model almost under all the same conditions; the optimal monitoring soil depth of maize at jointing stage, tasseling-silking stage is 0-20 cm, and the optimal monitoring soil depth of milk-maturity stage is 20-45 cm; the ELM inversion model at 20-45 cm soil depth at milk-maturity stage has the best effect, the decision coefficients of modeling set and verification set are 0.825 and 0.750, respectively, the root mean square error are 1.00% and 1.32%, respectively, and the normalized root mean square error are 10.85% and 13.55%, respectively. The combination of full subset selection method and machine learning can improve the inversion accuracy and robustness of SWC. This study provides a new way for rapid and accurate monitoring of SWC in farmland and precise irrigation. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The temperature in edible fungi greenhouse has the characteristics of time-variant, nonlinear and multi-coupling, so accurate and effective temperature predictions can effectively help growers adjust the greenhouse environment and prevent edible fungus production and quality decline. Based on the perspective of mining the time-series information in historical temperature data. This paper described the specific steps to realize the MA-ARIMA-GASVR-based hybrid combination method to predict the temperature in the edible fungus greenhouse. Firstly, we assumed that the historical temperature series data of edible fungus greenhouse was a dynamic combination of linear and non-linear components, the historical temperature sequences were decomposed into linear sequences and residual sequences using the moving averages (MA) method. Then, time series analysis was conducted to established the model of the autoregressive integrated moving average (ARIMA) by using linear sequence after the decomposition of the moving averages, and the future trend of linear sequences was predicted by the established model. Afterward, to better fit the relationship between temperature trends and various noises in the environment, the autoregressive integrated moving average model prediction value, the historical residual data and the historical temperature data were employed as the input of the support vector regression (SVR) model, and the genetic algorithm (GA) was used to optimize the parameters of the support vector regression model to improve its performance, the parameters being optimized are penalty parameter and radial basis function kernel parameters in the support vector regression model. Finally, the hybrid model output was the temperature prediction value which was more in line with the actual situation. Moreover, the hybrid method was verified using the experimental data from the edible fungus greenhouse in Beijing. In this paper, a representative edible fungus greenhouse was selected as the experimental object according to the observation time requirements and the time-varying needs of edible fungus greenhouse temperature, which was located in the Daxing District of Beijing. A total of 2 208 measured edible fungus greenhouse temperature data were collected from July 1st, 2019 to September 30th, 2019 during the experiment. The experimental data acquisition device used the JXBS-7001 temperature monitoring sensor was used to automatically collect and record the experimental data. Three sets of sensors were deployed in the edible fungus greenhouse to record the experimental data set which included the average temperature data. We trained the proposed model by using data from July 3rd, 2019 to July 16th, 2019 and forecasted the temperature of the next two days and compared temperature prediction experiments with different models and different time intervals. The results indicated that the MA-ARIMA-GASVR-based hybrid model could better fit the original temperature data, the mean squared error, the mean absolute error and mean absolute percentage error of an hour interval temperature were 0.18, 0.36, 1.34, and three error evaluation indexes all showed that the prediction accuracy of the hybrid method in this paper was better than the single models of support vector regression and support vector regression optimized by genetic algorithm, and it was also superior to the hybrid methods which were not processed by moving averages method or optimized by genetic algorithm. Besides, the mean squared error, the mean absolute error and mean absolute percentage error of 6hours interval temperature were 0.29, 0.52, 1.95. the hybrid method in this paper can satisfy the multi-step prediction within 6 hours, which could provide more time for edible fungus producers to adjust the temperature in the greenhouse. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, energy performance of a loop-heat-pipe (LHP) type solar photovoltaic/thermal (PV/T) heat pump water heating system is studied to evaluate its feasibility in three different climatic regions. A mathematic model of this system in our former study is built and validated using outdoor test data. On the basis of this model, influences of main structural design parameters including the installation angle and the orientation of the PV/T collector/evaporator are discussed and main parameters are optimized to further improve system operation performance. Based on optimal design parameters, economic feasibility of the proposed system under different weather conditions is analyzed using the life cycle cost (LCC) method. The system is integrated with solar PV/T, loop heat pipe and solar assisted air source heat pump technologies. This combined approach is benefit for improving solar energy comprehensive application efficiency of conventional solar PV/T systems. Moreover, it enlarges the application region of traditional air source heat pump water heating systems. Depending on different solar radiation conditions, this system can operate in different modes including solar photovoltaic LHP mode, solar assisted air source heat pump mode and the only air source heat pump mode. In this study, Beijing, Shanghai and Guangzhou were selected as typical representative cities in cold area, hot summer and cold winter area, hot summer and warm winter area. Typical meteorological year (TMY) data of three cities were extracted from TRNSYS. Based on TMY data, annual operation performance of the proposed system is calculated through the validated dynamic mathematic model. Firstly, PV/T collector/evaporators of three systems are all fixed in the south direction and the same installation angle (35°) was chosen. In this case, annual net power consumptions and solar heating fractions of three systems are calculated and compared. Then installation angles and orientations are optimized to ensure maximum solar energy application. The investigation results show that, among three cities, the solar heating fraction in Guangzhou is the largest. And the least electricity is consumed in Guangzhou, which is decreased by 43.9% and 44.7% respectively compared with those in Beijing and Shanghai. The comprehensive photothermal efficiency in Guangzhou is 8.19%, 5.58% higher than those in Beijing and Shanghai. Therefore, from the view of solar energy comprehensive efficient utilization, the application of the system is most proposed in Guangzhou, and then followed by Shanghai and Beijing. Considering impacts of installation angles and orientations, the ideal installation inclination of the system in Beijing, Shanghai and Guangzhou are 35°, 25°, and 35°, and the optimal installation directions of Beijing and Shanghai are facing the south, while that of Guangzhou is 30°east to the south. With the optimal parameters, it is found that solar heating fractions in Beijing and Guangzhou (i.e. 57.42% and 58.45%) are slightly higher than that in Shanghai. It is concluded that influences of two structural parameters are different for such system in different climatic areas. To ensure maximal solar energy application, it was necessary to optimize these two parameters. For the life cycle cost analysis, a traditional air source heat pump hot water heating system is chosen as the base system. The analysis results indicate that the initial investment of the system increases significantly, which is 2.5 times of that of the base system. However, the annual power consumption of the system is about half of that of the traditional system. Besides, solar power supply fractions in Beijing, Shanghai and Guangzhou are 53%, 51% and 66% respectively. As a result, the total operation and maintenance fees in the life cycle drop significantly, which are reduced by 67.86%, 59.00%, and 62.21% in Beijing, Shanghai and Guangzhou. The life cycle cost is correspondingly reduced by 58.75%, 49.83% and 53.09% in three cities. In conclusion, the application of this system is feasible for considered weather conditions in terms of both the operating performance and economic benefits. [ABSTRACT FROM AUTHOR]

As the city is developing continuously recently, frequently occurred haze has increasingly raised residents’ concerns about the urban ecological problems. Motor vehicle exhaust gas is one of the major sources of atmospheric particulates. The road green system, an important ecological measurement, which plays a significant role in preventing the motor vehicle exhaust gas diffusion, is of great significance to improve urban air quality and residents’ life quality and the living environment. For the purpose of urban road greening plan, we used high resolution remote sensing images and street views to compile the road information tables so as to select six roads (Baiziwan Road, Linda North Road, Jiaodaokou East Avenue, Dongzhimen North Street, and Nongzhanguan South Road) in Beijing city as the sample set which represent different road green systems, green configurations and street aspect ratios. For the simulation experiment in this paper, we used three-dimensional microclimate software ENVI-met, which was based on fluid and thermodynamics (the calculation of the basic law of the fluid dynamics model), to simulate the interaction processes among ground, vegetation, building and atmosphere in a small-scale urban space. We constructed three-dimensional main model area and set main characteristic parameters of the sample places, such as building, greening and underlying surface structure, and achieved the numerical simulation of interactions among ground, vegetation, building and atmosphere by using the weather prediction subprogram to analyze governing equation. We conducted field experiment in order to compare it with the numerical simulation results. The monitoring points were set on the roadside and behind the tree respectively. Each experiment street was set with one monitoring section which has two monitoring points at the height of 1.5 meters. We selected PM10 as the monitoring item and measure meteorological factors such as wind speed, wind direction and temperature at the same time. We set the results of the non-vegetation particulate matter as background value and superposed it with the diffusion of that under different green configurations to analyze the law of particulate matter diffusion of different green configurations. The simulation results showed that, under the same condition of wind speed and wind direction, the changes of PM10 concentration on the leeward side of monitoring point were higher than that on its windward side, which proved that the motion of airflow caused the particulate matter accumulation on the leeward side and the spreading of particulate matter over the road as air flow rising, so the concentration of particles on the leeward side was the largest, and then gradually decreased as the building height increasing. Under the same condition of aspect ratio, the effect of arbor--shrub-grass configuration on reducing PM10 concentration was the most obvious, followed by the arbor--shrub configuration. The effect of arbor on reducing PM10 concentration was weak and the effect of shrub on that was the weakest. Under the same condition of green configurations, the smaller the street’s aspect ratio (the wider the street) was, the greater decrease in the concentration of PM10, which indicated that the wider street was favorable for ventilation and had the lower the concentration of pollutants and vice versa. Comparing the measured data with the numerical simulation data, the coefficient between the measured value and the simulated value was 0.9894, and the correlation between the simulated results and the field observation results was pretty high, so it could be proved that the convection-diffusion model that ENVI-met used was available for simulating the diffusion of atmosphere particulate matter and can give reasonable calculation results of the law of the diffusion of atmosphere particles. [ABSTRACT FROM AUTHOR]

The evaluation of arable land quality is one of the important means to protect and manage arable land. In order to meet the needs of current arable land management work and protect the safety of national arable land resources, it is necessary to establish an arable land quality index system to evaluate the arable land quality. Based on the change of human demand for arable land resources, the components of arable land use system and the relationship between various components of arable land use system, this paper systematically expounded the theoretical framework of arable land quality evaluation，and the evaluation index system of arable land quality was established by the matching of different demand hierarchy characteristics of human and supply of arable land resources. The evaluation index system of arable land quality consisted of five dimensions: geology characteristics, soil traits, farming conditions, health status of arable land and biological characteristics of arable land. According to the different characteristics of each dimension of arable land quality, suitable evaluation methods were selected. The three dimensions of geology characteristics, soil traits and farming conditions were calculated by using the weighted additive. The health status of arable land is calculated by the “1+X” cumulative model; the biological characteristics of arable land were calculated by coefficients. Then, the quality of arable land for high yield and high efficiency demand was evaluated by the method of additive, and the quality of arable land for green health and sustainable development was evaluated by the method of revised. The quality of arable land in Daxing District of Beijing was evaluated by using multi-source and multi-scale data. The results showed that the evaluation results of arable land quality in Daxing District were divided into five grades at equal intervals of 20 points. The arable land quality index [80,100] was excellent, [60, 80) was good, [40, 60) was medium, [20, 40) was lower, and [0, 20) was poor. Under the conditions of high yield and high efficiency, the excellent area accounted for 88.27% of the total arable land, the good accounted for 11.73%. Under the condition of green health demand, the excellent accounted for 73.61% of the total arable land, and the good accounted for 26.39%. Under the conditions of sustainable development demand, the excellent accounted for 3.03% of the total arable land, and the good accounted for 52.30%, medium accounted for 40.39%, and lower accounted for 4.27%. From the perspective of space, the results of arable land quality evaluation of high yield, high efficiency, green health and sustainable development demand were all high in the east and low in the west. By comparing the results of arable land quality evaluation under different demand conditions, the area of arable land in excellent is decreasing, while the area of arable land in good, medium and lower is increasing. The health status of arable land and the biological characteristics of arable land have become important factors affecting the quality improvement of arable land. Research conclusions are that the evaluation index system of arable land quality based on sustainable development demand can provide effective support for the evaluation and management of arable land, and is more in line with the national quantity, quality and ecological management needs of arable land. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)