Electrostatic spraying has been widely used to enhance the droplet deposition under plant protection using UAV (unmanned aerial vehicles). The plant protection droplets can also move through the intense rotor under downwash and turbulence, when the droplets can carry sufficient electric charge. However, the CMR (charge-to-mass ratio) can attenuate markedly before reaching the crop canopy. Field-scale quantification of CMR has been constrained to deploy over a wide spray footprint, due to the conventional rigid Faraday cylinder collectors. In this study, a flexible AFC (aluminum foil container) was developed to collect the charged droplets under field conditions. The CMR was then quantified in contact charging UAV electrostatic spraying. A coupled attenuation model was further used to interpret the relative contributions of the charge loss and droplet mass loss during flight. A high-voltage generator module was integrated into a quadrotor spray platform and then configured for contact charging. The negative high voltage output was connected to a metal contact electrode inside the liquid tank in charge of the working solution. While the positive terminal was mounted on the landing gear and discharged to air, providing for the weak capacitive coupling that supported charge balance during hovering. The AFC collector followed the Faraday cage principle. But the rigid collectors were replaced with a flexible aluminum foil conductive layer supported by an insulating backing, allowing for the collector geometry to match the spray swath. Repeatability tests under identical spraying showed that the consistent CMR was comparable to a conventional Faraday cylinder in the field deployment. Field experiments were conducted at the charging voltages from 15 to 35 kV and the flight heights from 2.0 m to 5.0 m. Two spray adjuvants were evaluated at the same recommended concentration of 1‰, a surfactant-based product. Momentive Agrospred 910 and a modified plant oil product, Maifei, were used in the field test. Baseline CMR increased with the voltage and then reached 3.50 mC/kg at 35 kV. Field measurements indicated that there was an outstanding CMR loss in the rotor downwash, indicating the height effect. Once the flight height increased from 2.0 m to 5.0 m, the average CMR attenuation rates were 14.09 %, 24.23 %, 39.32 %, and 52.84% at 2.0, 3.0, 4.0, and 5.0 m, respectively. Rotor wind speed during measurements remained close to 14m/s. A shared background served as in the treatments. Model fitting showed that the exponential charge decay dominated the CMR attenuation from 2.0 m to 5.0 m, while there was a weak evaporation-driven mass loss. The charge attenuation coefficient, λ, was fitted approximately 0.16 at 35 kV, whereas the mass loss coefficient, K' prime, was approximately 4.0×10^-4, corresponding to less than 0.003 correction within 5.0 m. The λ increased from 0.08 to 0.16 over the voltages, as the voltage rose from 15 to 35 kV, with the intermediate values of 0.10, 0.12, and 0.14 at 20, 25, and 30 kV. Despite the high decay per unit distance at the higher initial charge, the higher voltage still produced the higher retained CMR in the typical operating heights. Both adjuvants increased baseline CMR for the retained CMR under UAV operation. Momentive increased the baseline CMR by about 17.71% at 35 kV, with the maximum of 4.12 mC/kg, whereas Maifei increased the baseline CMR by about 45.43 % with the maximum of 5.09 mC/kg. Attenuation rates were broadly comparable with/without adjuvants. The primary benefit of the initial CMR of the more charge remained after in-flight decay. Overall, the droplet charging model was established for contact charging, while the CMR attenuation model was for UAV electrostatic spraying. The AFC effectively captured the CMR variations dominated by flight height and rotor wind in the UAV crop protection spraying. The CMR can play an enhancing role in spray adjuvants. These findings can provide an experimental reference to optimize the UAV electrostatic spraying.

Electrostatic spraying is expected to integrate with unmanned aerial vehicle (UAV) platforms in modern agriculture. This approach represents an innovative plant protection technology, offering promising potential for improving pesticide utilization efficiency, deposition uniformity in precision agriculture. Electrostatic spraying imparts electrostatic charges to droplets through the application of high-voltage electric fields, thereby modifying their trajectories and enhancing adhesion to plant surfaces. This is particularly beneficial for achieving deposition of the abaxial (underside) surfaces of leaves, which are typically difficult to reach using conventional spraying techniques. This review aims to examine the development history, theoretical foundations, and current research on electrostatic spraying for crop protection UAVs. It systematically outlines the three mainstream charging mechanisms—corona, inductive, and contact charging—highlighting their physical principles, advantages, limitations, and the applicable voltage ranges. Inductive charging dominates current applications due to its relative safety and engineering simplicity. However, contact charging also presents distinct advantages over other mechanisms. The droplet charging process is relatively mild, avoiding intense discharges that may occur in corona charging systems. Since the sprayed liquid comes into direct contact with the electrode, a stable charge-transfer field is established, leading to more sufficient and uniform charging of droplets. In addition, the structural designs of inductive charging nozzles are discussed, including various electrode configurations (e.g., ring-type, cone-type, and embedded parallel plates), selection of electrode materials (such as copper, nickel, and stainless steel), and the integration of air-assisted mechanisms. Furthermore, key evaluation techniques are reviewed, including charge-to-mass ratio (CMR) measurements, droplet size characterization (e.g., volume median diameter, VMD), and deposition detection methods. However, standardized testing protocols remain lacking, and significant discrepancies persist between laboratory measurements and field performance—especially under complex field conditions involving wind, temperature, and humidity variations. The droplet behavior of electrostatic spraying is further analyzed through droplet trajectory modeling, with emphasis on three dominant electrostatic field interactions: 1) induced fields between droplets and plant targets, 2) repulsive fields among charged droplets, and 3) externally applied fields between the nozzle and the target. Each mechanism contributes differently to droplet motion, distribution, and deposition efficiency. Evidence from recent studies suggests that combining electrostatic spraying systems with UAV platforms can effectively improve spray characteristics, such as deposition density, spray width, total deposition, and pest control performance. However, their trajectory control capability remains limited and challenging:1) rotor-generated airflow interferes with droplet trajectories, weakens electrostatic adhesion, and accelerates CMR decay; 2) variations in UAV flight height affect the electric field distribution between the nozzle and the crop canopy, reducing deposition accuracy; and 3) environmental factors such as wind, temperature, and humidity introduce uncertainties that compromise field performance. To address these limitations, several recommendations for future research are proposed: 1) advance high-voltage contact or corona charging systems with enhanced safety features; 2) develop electrostatically optimized-size nozzles and adjuvants; 3) refine evaluation metrics by integrating CMR with droplet size distribution; and 4) conduct large-scale, crop-specific field validations. With the advancement of UAV technology, high-voltage electrostatics, and system miniaturization, UAV electrostatic spraying is poised to become a key tool for next-generation precision pesticide application, offering strong potential for reducing pesticide use and supporting sustainable agriculture.

High accuracy and efficiency can often be required to assess the freeze damage of the winter wheat. However, the traditional field phenotypic identification can be confined to strong subjectivity, serious deviations between laboratory simulations and field data, and the lengthy cycle of physiological and biochemical detection. In this study, a freeze damage classification was proposed to fuse the vegetation indices, texture, and color features. A freeze damage model was also constructed to combine with the machine learning. The unmanned aerial vehicles (UAVs) were utilized to collect the multispectral and visible light imagery. Specifically, the UAVs were equipped with multispectral sensors and high-resolution visible light cameras. The optimal weather (clear skies, stable and light) was employed to ensure the data quality. A total of 15 vegetation indices were extracted, including the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), excess blue index (ExB), and soil adjusted vegetation index (SAVI). The spectral reflectance was also selected from the specific wavelength bands in order to effectively represent the vegetation’s photosynthetic capacity and canopy health status. Furthermore, the gray-level co-occurrence matrix (GLCM) was employed in texture feature analysis. Some parameters were calculated to quantify the spatial distribution of the pixel gray levels in the imagery, such as the mean, variance, contrast, and homogeneity from the GLCM. These texture features were then captured for the subtle structural variations in the wheat canopy caused by freeze damage. Additionally, the color features were derived from the RGB color space of the visible light images. Statistical parameters were computed to represent the visual color of the winter wheat under freeze damage, such as the color mean, standard deviation, and color moment. Feature screening was conducted using the Pearson correlation coefficient (PCC) method and the minimum redundancy feature selection algorithm (mRMR). Firstly, the Pearson correlation analysis was used to measure the linear correlation between each feature and freeze damage levels, thus eliminating the features with a low correlation. Subsequently, the minimum redundancy feature selection was optimized the feature set, in order to balance the feature relevance and redundancy. The final features were selected for the informative and non-repetitive ones. Four classification models were then constructed: random forest (RF), support vector machine (SVM), backpropagation neural network (BPNN), and eXtreme gradient boosting (XGBoost). Each model was also trained with tunable parameters. For example, the XGBoost model was utilized to adjust the parameters, such as the learning rate, the number of estimators, and the maximum depth after cross-validation. The experimental results demonstrated that the multi-source feature fusion significantly improved the performance of the model. Among them, eXtreme Gradient Boosting (XGBoost) showed the optimal performance under the fusion of vegetation indices, texture, and color features. The accuracy, recall, precision, and F1-score reached 0.73, 0.68, 0.71, and 0.69, respectively. Further analysis revealed that the texture features shared the highest correlation with the manually surveyed levels of the freeze damage. The structural damages of the canopy were also sensitive to capture, as the freeze damage often disrupted the orderly arrangement of the wheat leaves and stems. The texture features were effectively detected after capture. The multi-source remote sensing data fusion was validated to assess the freeze damage. The spectral, texture, and color information were integrated to precisely monitor the freeze damage of the winter wheat for disaster prevention decision-making. Remote sensing and machine learning were also applied to assess agricultural disasters in smart agriculture. The finding can serve as a practical reference for crop stress monitoring in the disaster early warning of intelligent agriculture.

Here, the ZAL-YOLOv8n model was proposed to rapidly and accurately detect the apple appearance quality in natural scenes using the improved YOLOv8n model. The baseline model was selected as the YOLOv8n model, the lightest model in the YOLOv8 series. Subsequently, the improved model was deployed on the mobile edge devices, despite its relatively lower accuracy of recognition. The apple appearance quality was classified into three categories: Immature, Mature, and Fall ill, according to the influencing factors, such as the fruit maturity and health. When the fruit with the greenish-blue peel was marked as “Immature”. Once the peel was red or a mix of red and yellow, the fruit was marked as “Mature”. If there were disease spots on the peel, the fruit was marked as “Fall ill”. Two common apple diseases, namely “Ring rot” and “Scab”, were further identified from the disease category. Firstly, the partial convolution (PConv) and the efficient multi-scale attention mechanism (EMA) were integrated as the EP-C2f module, in order to replace the C2f module in the backbone network. The EMA attention mechanism was employed to redistribute the attention weights, and then group all channels with the same number of channels. Effective information on all channels was retained to realize the interaction between channel and spatial position. Thereby the target area was focused on enhancing the feature extraction for apples under complex occlusion. Simultaneously, the dynamic convolution of the PConv was used to filter out the effective feature data. The model size and parameters were reduced after extraction. Secondly, the MPDIoU was introduced as the boundary regression loss function, in order to realize the accurate positioning of apple skin lesion areas in diseased apples. The reason was that the original boundary box regression loss function (CIoU) of the YOLOv8 model failed to realize the rapid fitting, where the types and degrees of pathogen infections varied among diseased apples. The new position fitting was accelerated to distinguish and locate the lesion edges, in order to minimize the distance between the upper left and lower right corners of the predicted and the real box. Finally, the Slim-neck architecture was utilized to reconstruct the feature fusion network of the YOLOv8 model. The lightweight of the neck network was achieved to enhance the operating speed. The experimental results indicate that the accuracy, recall, and mean average precision of the ZAL-YOLOv8n model increased by 3.4, 1.1, and 1.3 percentage points, respectively. Meanwhile, the floating-point operations, parameter quantity, and model size were reduced by 22.2%, 17.7%, and 15.9%, respectively, fully meeting the deployment requirements for the mobile edge devices. The ZAL-YOLOv8n model was performed on the highly precise detection and quality identification of the apples with different appearance qualities in natural scenes. The high degree of lightweight was also achieved to balance between the accuracy and speed, in order to realize the real-time detection on the low-computing-power devices. Therefore, the improved model can also provide technical support to the research and development of intelligent robots for apple picking.

Green lawns can play a crucial role in the landscape and ecological environments with the rapid urbanization in China. However, there is an ever-increasing demand for the daily maintenance of lawns. Among them, the heterogeneous weeds can often compete with the native grass for nutrients and growing space in lawns, leading to the low overall quality of urban greenery. The aesthetic appeal of the lawn can also be diminished after premature aging. Thereby, the accurate and rapid detection of the weed can be required for deep learning and computer vision. This study aims to improve the efficiency and accuracy of the detection of heterogeneous weeds in natural environments. A lightweight algorithm was also proposed using the original YOLOv8n model. Firstly, the deformable convolutional network v2 (DCNv2) was employed to combine with the c2f convolutional layers in the backbone network. The offsets were then introduced to enhance the feature extraction of the improved model. Different regular shapes of the heterogeneous weeds were captured after optimization. Additionally, a modulation mechanism was incorporated to control the contribution of each sampling point to the output. The precision and robustness were then improved to focus more on the target regions, thereby reducing the interference from background noise. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced in the neck network. Multi-scale features were efficiently fused using bidirectional cross-scale connections and weighted feature fusion. The targets were detected at varying scales. Furthermore, the BiFPN significantly reduced the computational overhead, compared with the traditional feature pyramid network (FPN). The redundant connections were also eliminated to incorporate the lightweight weighting mechanisms. The efficiency and generalization of feature fusion were also improved after feature fusion. Lastly, the traditional intersection over union (IoU) loss function was replaced with the inner-IoU (inner intersection over union) loss function. The objective function of the bounding box regression was then optimized to learn the target location information, thereby improving the convergence speed and detection performance. More precise guidance was realized for the improved model. The experimental results show that the improved YOLOv8-LDB model reduced the number of parameters, the computational cost, and the model size by 32.7%, 13.6%, and 31.8%, respectively, compared with the original YOLOv8n. While the mean average precision (mAP) increased by 3.2 percentage points. The performance of the improved YOLOv8-LDB model was better than that of the seven commonly used network models (including Faster-RCNN, SSD, YOLOv5s, YOLOv5n, YOLOv7n, YOLOv10n, and YOLOv11n), in terms of the precision, parameter count, computational cost, and model size. The mean average precision was improved by 21.6, 9.6, 1.6, 7.7, 2.4, 4.1, and 3.3 percentage points, respectively. Additionally, the detection speed increased from 80.4 to 87.1 frames per second. The inference efficiency was also enhanced for the real-time detection of heterogeneous weeds in natural environments. The YOLOv8-LDB algorithm demonstrated superior performance across multiple metrics. Automatic sprayers and weeding robots can be integrated to realize variable-rate precision spraying and targeted weed control. The automation level of lawn management can also be further advanced in the future. The lightweight improved model can be expected to find wide applications in smart city greening and precision agriculture. The findings can also provide technical support to protect the sustainable ecological environment in urban areas.

Ginger diseases and pests have posed a serious threat to the yield in recent years. However, the artificial and mechanical application cannot fully meet the large-scale production at present, due to the slow overall progress and the low degree low of intelligence. This study aims to develop the intelligent application equipment of ginger for the high efficiency and accuracy of the intelligent vehicle. A lightweight model was proposed to realize the high-performance deployment of the ginger leaf disease and pest detection on mobile terminals using improved YOLOv5s. Ghost module of GhostNet was selected to replace the convolutional layers in the original YOLOv5s neural network, except the first layer. Ghost BottleNeck was used to replace the Resunit residual component in the original C3 concentrate-comprehensive convolution block. The lightweight of the network model was obtained to reduce the number of parameters and the amount of calculation. At the same time, the memory consumption was reduced in the model weight file. CA attention mechanism module was added after the C3 block in the feature fusion network, in order to improve the recognition and positioning accuracy. The reason was that the lightweight of the model caused the feature loss, when the neural network was used to extract the features of the image. The experimental results show that the number of parameters of the improved YOLOv5s model was 3.76×10⁶M, which was 52.0% of the original. The computational complexity was 8.4G, which was 50.6% of the original. The size of the weight file was 7.79MB, which was 55.2% of the original. The average precision and average precision reached 80.5% and 83.8%, respectively, which were 1.3 and 1.5 percentage points higher than those of the original model. The improved model was greatly reduced the number of parameters, calculation amount and weight file size for the high detection accuracy, compared with Fast-RCNN, SSD, YOLOv4, YOLOv5s and Tea-YOLOv5s target detection models. The missed and false detection of image targets were also reduced, compared with the YOLOv5s model. And the improved network model was required less hardware conditions. The performance of the improved model was verified on mobile terminals. The Ginger-YOLOv5s model was deployed on the Jetson Orin NX development board, where the detection code was rewritten in C++. The model was accelerated using TensorRT high-performance operator, Int8 quantization processing, CUDA rewriting preprocessing and multi-thread processing. The final frames per second reached 74.3, which was fully met the requirements of operation efficiency in the application machinery for the real-time detection of ginger leaf diseases and pests. The finding can provide the technical support for the migration and deployment of the model on the ginger application vehicle.

In order to solve the problems of complex detection scene, low detection accuracy and high computational complexity of tomato leaf pests and diseases in natural environment, a deep learning algorithm SLP-YOLOv7-tiny was proposed. Firstly, part of the 3×3 convolution Conv2D Convolution in the backbone feature extraction network is changed to distributed migration convolution DSConv2D (2D Depthwise Separable Convolution) to reduce the computing load of the network and speed it up. Less memory usage; Secondly, the parameter-free attention module (SimAM) was integrated into the backbone feature extraction network to enhance the model's ability to effectively extract and integrate features of pests and diseases. Finally, the original YOLOv7-tiny CIOU loss function is replaced with Focal EIOU loss function to accelerate the model convergence and reduce the loss value. The test results show that The overall identification accuracy, recall rate, average accuracy mAP_0.5 (the average accuracy when the IOU threshold is 0.5), and mAP_{0.5 ~ 0.95} (the average accuracy when the IOU threshold between 0.5 and 0.95) of SLP-YOLOv7-tiny model are 95.9%, 94.6%, and 98%, respectively Compared with YOLOv7-tiny before the improvement, they respectively increase by 14.7, 29.2, 20.2 and 30 percentage points. Meanwhile, the calculation amount decreases by 62.6% and the detection speed increases by 13.2%. Compared with YOLOv5n, YOLOv5s, YOLOv5m, YOLOv7, Yolov7-Tiny, Faster-RCNN and SSD target detection models, mAP_0.5 improved by 2.0, 1.6, 2.0, 2.2, 20.2, 6.1 and 5.3 percentage points respectively. The computing capacity was only 31.5%, 10.6%, 4.9%, 4.3% and 3.8% of YOLOv5s, YOLOv5m, YOLOv7, father-RCNN and SSD. The results show that SLP-YOLOv7-tiny can accurately and quickly detect tomato leaf diseases and pests, and the model is small, which can provide certain technical support for the development of rapid and accurate detection of tomato leaf diseases and pests.SLP-YOLOv7-tiny can be expected to accurately and rapidly detect the tomato leaf diseases and insect pests. The small model was more conducive to the migration application. A comparison was also performed on the detection performance of different attention mechanisms. The SimAM attention mechanism shared the better screening of effective feature information on the YOLOv7-tiny model. The disease spots were identified to effectively improve the accuracy of the model, compared with the SENet and CBAM attention mechanism. The ablation test was carried out to verified the added module. The high detection accuracy and computational efficiency were suitable for the deployment in the natural environment with the limited computing resources, such as mobile terminals. The visualization of model detection show that the SLP-YOLOv7-tiny model can be used to learn and distinguish the fine features of tomato leaf disease spots. The detection accuracy was also better than that of the YOLOv7-tiny model. Moreover, the SLP-YOLOv7-tiny model can be used to accurately identify the diseases and pests of diseased tomato leaves in the natural shooting environment. The high detection accuracy can be applied for the multiple diseases and pests. The better identification was also obtained for the small features in the similar diseases and pests. The finding can provide the technical support for the rapid and accurate detection of tomato leaf diseases and insect pests.

To improve the innovation of agricultural machinery product styling, this paper proposes a shape structure behavior function (SSBF) model suitable for the industrial design field. The feature line evolution method combining shape grammar and genetic algorithm was used for modelling the of the grader, which not only maintains the product style characteristics but also reflects the typical identification characteristics of the bionic prototype and produces a new product modelling scheme. By conducting cognitive and recognition experiments on product styling features, the ranking of product styling features and the contribution of each component to product styling were determined. The method of combining shape grammar and quadratic Bézier curve was used to express and encode feature lines, and genetic algorithm was used to evolve biomimetic forms to form product feature lines with typical biological morphological features; The extracted form bionic elements were integrated into the grader modelling design, and the interaction evaluation was carried out through the genetic algorithm evolution scheme. The basic form elements were extracted and analyzed, and the deduction rules were formulated and reorganized. The derived feature line geometric data considered the product’s image features and the bio-inspired prototype, which can be used for the follow-up guidance of industrial design schemes.