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The emergence of ResNet provides a powerful tool for training extremely deep networks. The core idea behind it is to change the learning goals of the network. It no longer learns new features from scratch but learns the difference between the target and existing features. However, the difference between the two kinds of features does not have an independent and clear meaning, and the amount of learning is based on the absolute rather than the relative difference, which is sensitive to the size of existing features. We propose a new network that perfectly solves these two problems while still having the advantages of ResNet. Specifically, it chooses to learn the quotient of the target features with the existing features, so we call it the quotient network. In order to enable this network to learn successfully and achieve higher performance, we propose some design rules for this network so that it can be trained efficiently and achieve better performance than ResNet. Experiments on the CIFAR10, CIFAR100, and SVHN datasets prove that this network can stably achieve considerable improvements over ResNet by simply making tiny corresponding changes to the original ResNet network without adding new parameters. [ABSTRACT FROM AUTHOR]

In response to the problems of low control accuracy, single detection of operating parameters, and insufficient collaborative control of unmanned fertilization in field fertilization operations, this paper proposes an adaptive control strategy for fertilizer discharging shaft speed based on segmented linear interpolation method. By constructing a relationship model between fertilizer discharging shaft speed and motor control signals in different speed ranges, the on-site self-calibration of fertilizer discharging shaft speed and the precise control of the fertilization rate is achieved. At the same time, real-time detection and warning technology for multiple working condition parameters were integrated, and a bus communication protocol between unmanned tractors and variable-rate fertilizer applicators was developed. A variable-ratefertilization monitoring system based on unmanned tractors was developed, and actual experimental tests were conducted to test the system's performance. Among them, the calibration test results of fertilization rate showed that the discharging rate of the fertilizer apparatuses (p) was negatively correlated with the rotation speed of the fertilizer discharging shaft, and that the installation position of the fertilizer apparatuses affected the uniformity of fertilization between the rows of the fertilizer applicator. The speed response test of the fertilizer discharging shaft showed that the average response time (Ts) of the fertilizer discharging shaft speed controlled by the self-calibration model was 0.40 s, the average steady-state error (ess) was 0.13 r/min, and the average overshoot (σ) was 7.33%. Compared with the original linear model, the ess was reduced by 0.23 r/min, and the σ was reduced by 1.54 percentage points. The results of the fertilization status detection tests showed that the system can achieve real-time detection of different operating parameters and states, as well as collaborative control of tractors and fertilizer applicators. The results of the fertilization rate control accuracy test showed that the average fertilization control error of the system was 1.91% under different target fertilization rate, which meets the requirements of variable-rate fertilization field operations. This study can serve as a technical reference for the design and development of fertilization robots in the context of unmanned farm development. [ABSTRACT FROM AUTHOR]

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the effects of soil penetration resistance, tillage depth, and operating speeds on the deformation and fatigue of the subsoiling shovel based on the real-time measurement of tractor-operating conditions data. Various types of sensors, such as force, displacement, and angle, were integrated. The software and hardware architectures of the monitoring system were designed to develop a field operation condition parameter monitoring system, which can measure the tractor's traction force of the lower tie-bar, the real-time speed, the latitude and longitude, tillage depth, and the strain of the subsoiling shovel and other condition parameters in real-time. The time domain extrapolation method was used to process the measured data to obtain the load spectrum. The linear damage accumulation theory was used to calculate the load damage of the subsoiling shovel. The magnitude of the damage value was used to characterize the severity of the operation. The signal acquisition test and typical parameter test were conducted for the monitoring system, and the test results showed that the reliability and accuracy of the monitoring system met the requirements. The subsoiling operation test of the system was carried out, which mainly included two kinds of soil penetration resistances (1750 kPa and 2750 kPa), three kinds of tillage depth (250 mm, 300 mm, and 350 mm), and three kinds of operation speed (4 km/h low speed, 6 km/h medium speed, and 8 km/h high speed), totaling 18 kinds of test conditions. Eventually, the effects of changes in working condition parameters of the subsoiling operation on the overall damage of subsoiling shovels and the differences in damage occurring between the front and rear rows of subsoiling shovels under the same test conditions were analyzed. The test results show that under the same soil penetration resistance, the overall damage sustained by the subsoiling shovels increases regardless of the increase in the tillage depth or operating speed. In particular, the increase in the tillage depth increased the severity of subsoiling shovel damage by 19.73%, which was higher than the 17.48% increase due to soil penetration resistance and the 13.07% increase due to the operating speed. It should be noted that the front subsoiling shovels consistently sustained more damage than the rear, and the difference was able to reach 16.86%. This paper may provide useful information for subsoiling operations, i.e., the operational efficiency and the damage level of subsoiling shovels should be considered. [ABSTRACT FROM AUTHOR]

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient ( R c v 2 ) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields. [ABSTRACT FROM AUTHOR]

A good planting bed is a prerequisite for improving planting quality, while complex ground excitation often leads to machine bouncing and operation vibration, which then affects the operation effect. In order to improve the quality of rotary tillage operations, it is necessary to study the effects of various vibration excitations on the unit during tractor rotary tillage operations and analyze the vibration interaction relationship among the tractor, the three-point suspension mechanism, and the rotary tiller. For this purpose, multiple three-way acceleration sensors were installed at different positions on the rotary tiller unit of a Lexing LS1004 tractor(Lexing Agricultural Equipment Co. Ltd., Qingdao, China) to collect vibration data at different operating speeds and conduct vibration characteristic analysis between different components. The test results showed that when the unit moved forward at 2.1 km/h, 3.6 km/h, and 4.5 km/h, respectively, the vibration acceleration of the tractor, the three-point suspension mechanism, and the rotary tiller increased with the increase in speed, and there was indeed interaction between them. The vertical acceleration change during the test in the three-point suspension mechanism was the most significant (5.914 m/s2) and was related to the increase in the speed of the vehicle and the vibration transfer of the rotary tiller. Meanwhile, the vertical vibration acceleration of the tractor's symmetrical structure was not similar, suggesting the existence of structural assembly problems. From the perspective of frequency domain analysis, the resonant frequency at the cab of the tractor was reduced in a vertical vibration environment, with relatively low frequencies (0~80 Hz) and small magnitudes, which might be beneficial to the driver's health. The rotary tillage group resonated around 350 Hz, and this characteristic can be used to appropriately increase the vibration of the rotary tiller to reduce resistance. The tractor cab resonated around 280 Hz, which must be avoided during field operations to ensure driver health and reduce machine wear. The research results can provide a reference for reducing vibration and resistance during tractor rotary tillage operations, as well as optimizing and improving the structure of rotary tillers and tractors. [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator of crop photosynthetic capacity and nutritional status, is of great significance for precise nitrogen fertilization management. To explore the existence of a versatile regression model that can be successfully used to estimate the LCC for different varieties under different growth stages and nitrogen stress conditions, a study was conducted in 2023 across the growing season for winter wheat with five species and five nitrogen application levels. Two machine learning regression algorithms, support vector machine (SVM) and random forest (RF), were used to establish the bridge between UAV-derived multispectral vegetation indices and ground truth LCC (relative chlorophyll content, SPAD), taking the multivariate linear regression (MLR) algorithm as a reference. The results show that the visible atmospherically resistant index, vegetative index, and normalized difference vegetation index had the highest correlation with ground truth LCC, with a Pearson's correlation coefficient of 0.95. All three regression algorithms (MLR, RF, and SVM) performed well on the training dataset (R2: 0.932–0.944, RMSE: 3.96 to 4.37), but performed differently on validation datasets with different growth stages, species, and nitrogen application levels. Compared to winter wheat species and nitrogen application levels, the growth stages had the greatest influence on the generalization ability of LCC estimation models, especially for the dough stage. At the dough stage, compared to MLR and RF, SVM performed best, with R2 increasing by 0.27 and 0.10, respectively, and RMSE decreasing by 1.13 and 0.46, respectively. Overall, this study demonstrated that the combination of UAV-derived multispectral VIs and the SVM regression algorithm could be successfully applied to map the LCC of winter wheat for different species, growth stages, and nitrogen stress conditions. Ultimately, this research is significant as it shows the successful application of UAV data for mapping the LCC of winter wheat across diverse conditions, offering valuable insights for precision nitrogen fertilization management. [ABSTRACT FROM AUTHOR]

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed. [ABSTRACT FROM AUTHOR]

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson's correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season. [ABSTRACT FROM AUTHOR]

To address the issues of excessive heat loss from the roofs of multi-span greenhouses and high energy consumption for heating during winter production, we propose an approach for the external insulation of the roof of multi-span glass greenhouses and have developed an external insulation system (EIS) to practice this approach. The system achieved full coverage of the greenhouse roof through mechanized unfurling and furling of external thermal blankets, thereby achieving energy-saving insulation. This paper describes the overall design and working method of the EIS, providing detailed design and structural parameters for critical components such as the traction rope transmission mechanism and the rail-type sealing structure. Through a system verification experiment, the specifications of the traction rope were determined and the rationality of the EIS's thermal blanket unfurling and furling time was confirmed. An insulation performance experiment indicated that the average heat flux of the greenhouse roof covered with the external thermal blanket over 14 continuous nights was 54.2 W/m2, compared with 198.6 W/m2 for a single-layer glass roof. Covering the roof with the external thermal blanket reduced heat loss from the glass roof by 72.7%. The average heat flux of the roof of the Venlo-type multi-span greenhouse with double-layer internal insulation was 99.9 W/m2 during the same period, indicating that the heat loss from the roof using external insulation was only 50.3%. This study provides a novel thermal insulation approach and an energy-saving system for multi-span greenhouses. [ABSTRACT FROM AUTHOR]

Poly(D,L‐lactide‐co‐glycolide)‐poly(ethylene glycol) (PLGA‐PEG) diblock copolymers with different compositions were synthesized by ring‐opening polymerization of D,L‐lactide and glycolide, using monomethoxy PEG as macro‐initiator. The resulting copolymers were characterized by nuclear magnetic resonance, gel permeation chromatography, and critical micelle concentration analyses. Self‐assembly of the copolymers yielded aggregates of different architectures, including spherical micelles and a mixture of spherical and worm‐like micelles with Y‐junctions. The self‐assembled architecture depends on both the hydrophilic/hydrophobic balance and the molar mass of copolymers. Valsartan, a widely used drug in the treatment of hypertension, was loaded in micelles using a co‐solvent evaporation method. High drug‐loading content was obtained for worm‐like micelles. in vitro drug release was performed at 37°C in pH 7.4 phosphate‐buffered saline. An initial burst release is detected in all cases, followed by slower release up to 9 days. The overall release rate is strongly dependent on the degradation of micelles. Copolymers with short PLGA blocks exhibit faster drug release due to faster degradation of micelles, and worm‐like micelles present slower drug release as compared to spherical ones. Therefore, PLGA‐PEG copolymer micelles with high drug‐loading capacity, different architectures, and variable drug release rates could be most attractive for sustained delivery of valsartan. [ABSTRACT FROM AUTHOR]