ObjectiveCitrus defect recognition is a key link in realizing automatic citrus fruit picking and controlling fruit quality. This study aims to improve the accuracy of citrus defect recognition in natural environments and achieve all-weather operation of intelligent picking.MethodBy optimizing key modules, an improved YOLOv7 algorithm was proposed. The specific improvements included introducing the complete intersection over union (CIoU) loss function to improve bounding box regression accuracy; adopting the HardSwish activation function to enhance network learning and computational efficiency; integrating the attention free transformer (AFT) to strengthen target feature recognition; combining the residual multilayer perceptron (ResMLP) and dynamic convolution (DC) technologies to improve model’s adaptability and stability under complex lighting conditions.ResultUsing a dual light source system, this algorithm achieved all-weather detection of citrus fruits and their defects in natural environments. It detected defects such as black spots and cracks under natural light or white light, while at night, violet light served as a complementary means to detect defects that were not obvious under white light or natural light based on fluorescent responses. The experimental results showed that the improved YOLOv7 algorithm achieved 97.9% recognition accuracy for citrus fruits and 92.8% for defects during daytime, which were 3.8 and 13.4 percentage points higher than those of the original YOLOv7 algorithm, respectively; the defect recognition accuracy at night reached 82.4%.ConclusionThe citrus defect recognition method proposed in this paper has high accuracy and a wide applicable time range, providing new insights for the intelligent harvesting in the citrus industry.

[Objective]To guide the rational application of pesticides,this study aims to develop a simple and reliable detection method to acquire real-time information on the deposition and distribution of pesticide droplet on crop leaves.[Method]This study proposed a method for detecting foliar fluorescent droplet deposition based on an improved YOLOv8.By screening fluorescent tracers and optimizing their mass concentrations,suitable experimental conditions for leaf droplet image acquisition were determined,and a corresponding dataset was constructed.Based on YOLOv8-seg,the AdamW optimizer was introduced,an efficient multi-scale attention(EMA)mechanism was embedded into the backbone network,and a semantics and detail infusion(SDI)module was incorporated into the neck structure to enhance detection performance.[Result]The fluorescence tracer screening results showed that the fluorescein(a yellow-green fluorescent tracer)solution with 1.0 g/L clearly revealed the spatial distribution of droplets on leaf surfaces.Field experiment results demonstrated that the improved model achieved mAP@0.50 and mAP@0.50-0.95 values of 95.4％and 73.1％respectively,in object detection tasks.For segmentation mask evaluation,the mAP@0.50 and mAP@0.50-0.95 reached 92.5％and 61.3％respectively,outperforming the baseline model in overall performance.[Conclusion]The improved YOLOv8-based method for fluorescent droplet deposition detection on leaf surface enables accurate droplet recognition and distribution analysis.It features simple operation and high stability,offering technical support for spray quality assessment and precision pesticide application.

Greenhouse tomato production is highly influenced by environmental variability, where root-zone temperature plays a crucial role in fruit growth, biomass accumulation, and energy-use efficiency, particularly under winter low-temperature conditions. This study aimed to develop and validate a root-zone temperature dynamic regulation strategy for cherry tomato cultivation under multifactor coupled greenhouse conditions. Four root-zone temperature treatments were initially established: control (CK, no temperature regulation), T15 (setpoint of 15℃), T20 (setpoint of 20℃), and T25 (setpoint of 25℃). An IoT-based monitoring system was constructed to continuously collect root-zone temperature, substrate electrical conductivity, substrate moisture, air temperature, air humidity, CO2 concentration, light intensity, and fruit diameter data. With environmental factors as inputs, four machine learning models—Support Vector Regression (SVR), Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost)—were developed and evaluated to predict fruit diameter growth. Taking fruit diameter growth as the optimization objective, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) were employed to establish production-oriented root-zone temperature dynamic regulation strategies under environmental uncertainty. For the field validation, the dynamic temperature strategy was set as a treatment (DT) and compared with CK and constant-temperature treatments regarding to plant growth and energy consuming. The results showed that SVR achieved the best predictive performance for fruit diameter growth, with an R² of 0.8568 and an RMSE of 0.5142. All three optimization algorithms generated highly consistent optimal solutions, confirming the reliability of the scenario-based optimization framework. GA converged rapidly in terms of iteration number under the peak-growth and yield-oriented objectives, SA maintained comparatively balanced computational efficiency across the three objectives, whereas PSO exhibited greater sensitivity to the characteristics of the optimization objective. The yield-oriented strategy, which maximized the expected mean fruit growth response across representative environmental scenarios, was selected for field validation because it was most consistent with the commercial objective of improving population-level fruit growth and overall greenhouse yield. Under fluctuating winter greenhouse conditions, root-zone temperature regulation generally improved plant and fruit growth compared with the unheated control. DT produced the largest observed functional leaf phenotype, followed by T15, while T20 and T25 showed intermediate leaf development and CK exhibited the smallest leaves. During the early fruit expansion stage, DT showed the fastest expansion rate, while DT, T15, and T20 achieved greater fruit diameter increments than CK and T25. All heated treatments substantially increased fruit fresh and dry biomass compared with CK, and the largest numbers were observed in DT. Among the heated treatments, cumulative heating duration decreased in the order of T25 (2038.11 h), T20 (1985.12 h), T15 (1813.10 h), and DT (1605.41 h). Overall, DT achieved the best integrated agronomic and energy-use performance, which validated the developed framework of root-zone temperature dynamic regulation strategy.

Lithium Battery Surface defect detection plays a critical role in industrial applications. The current detection tasks face two major challenges: (1) significant variation in defect scales, especially the difficulty in capturing and identifying tiny defects; (2) the need for the detection process to simultaneously meet strict standards of high accuracy and real-time performance. To address these issues, this paper proposes a Dual Attention Pyramid Segmentation Network (DAPSeg), which ensures high precision while achieving real-time segmentation of surface defects. The Selective Kernel Module (SKM) assists the backbone network in adaptively extracting multi-scale features according to the importance of different scales, addressing the significant scale variation in defect scenarios. In the design part of the segmentation head, a lightweight architecture is adopted. The Blueprint Separable Layer (BSL) aims to capture more semantic information from different levels of the encoder while further improving the model’s inference efficiency; the Dual Attention Feature Fusion Module (DAFFM) is used for multi-scale feature fusion, obtaining finer segmentation regions from both spatial and channel dimensions. Additionally, we use the diffusion model to generate additional image data on the proposed lithium battery surface defect segmentation dataset (LB-SD) to alleviate the overfitting problem of model training caused by the imbalance of different defect samples. Experimental results show that DAPSeg achieves mIoU scores of 79.57%, 83.53%, and 89.10% on LB-SD, MT, and MSD, respectively, with a processing speed of 74.09 FPS. Compared with other state-of-the-art models, DAPSeg strikes a good balance between accuracy and inference speed, while also demonstrating strong generalization performance.

In the litchi cultivation industry, the timely management of inferior fruits (such as cracking, decay, and browning) and precise regulation based on their prevalence are crucial for enhancing fruit quality and minimizing resource waste. To improve monitoring efficiency within litchi orchards, this study introduces an intelligent methodology for detecting inferior litchi fruits. We chose to improve on the basis of YOLOv8 algorithm to obtain an inferior litchi fruit detection algorithm in orchard environment, and named it YOLOv8-BL (Bad Litchi, BL) algorithm. The improvement method is as follows: firstly, the small target detection layer P2 is added to improve the model's ability to detect tiny targets. Secondly, the number of output channels in the large target detection layer P5 is cropped to reduce the number of model parameters. Then, the lightweight Mixed Local Channel Attention (MLCA) mechanism is integrated into the C2f module, and the C2f_MLCA module is obtained to enhance the network's ability to capture effective features. Then, the Wise-IoU loss function is used instead of the original loss function to balance the influence of images of different complexity on the performance of the model. Finally, Masked Generative Distillation (MGD) technique was adopted to use YOLOv8l-BL as a teacher model to guide the training of YOLOv8n-BL model, and the YOLOv8n-BLS model was obtained to improve the accuracy. In this study, the YOLOv8n-BLS model achieved a mean Average Precision at IoU 0.5 (mAP50) of 82.7 % in an orchard environment-14.3 % higher than that of the YOLOv8n model-with a reduction in parameter count by 16.7 %. This model also demonstrates superiority over other mainstream algorithms and significantly enhances the detection performance of inferior litchi fruits within orchard settings. The efficient Inferior litchi fruit detection model can provide litchi fruit farmers with a new intelligent management solution in practical applications.

Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection.

This article introduces an uncrewed aerial vehicle (UAV)-based multispectral model, YOLO-SPAD, for rapid, nondestructive estimation of the relative chlorophyll content of tea leaves. This is crucial for predicting growth conditions, implementing precise irrigation and fertilization, and increasing tea yields. The model leverages the spectral separation of the tea tree canopy and the YOLOv8 neural network architecture for image segmentation. Multispectral imagery of the standardized tea plantation was captured by a UAV equipped with a five-channel camera, while corresponding soil plant analysis development (SPAD) values were measured using a SPAD-502Plus device. Based on the YOLOv8 model, it is proposed to incorporate a spectral fusion (SF) module to better adapt to multispectral characteristics. Four deep learning models for image segmentation were further compared. The improved YOLOv8 model achieved a segmentation mIoU of 91.61% for the tea tree canopy, outperforming the YOLOv5 (90.65%) and DeepLabv3+ (86.14%), which were improved using the same method. Five different band combinations, 43 vegetation indexes, and 40 texture features were analyzed to construct mapping transformation pairs for reflectance in the segmented area. This was applied to the SPAD prediction head to implement YOLO-SPAD. The YOLO-SPAD model was used to predict tea canopy SPAD using UAV multispectral imagery with a coefficient of determination ( $R<^>{2}$ ) of 0.85 and a low error (root mean square error (RMSE) =2.14, mean absolute error (MAE) =1.79), providing accurate and stable predictions. This model supports dynamic monitoring of tea tree growth via UAV remote sensing, aiding crop nutrition improvement and precision agriculture implementation.

Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Using YOLOv8 large (YOLOv8l) as the base, the neck layer of the original network is replaced with an asymptotic feature pyramid network (AFPN) network to reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, is incorporated to improve the model's ability to extract features. The backbone network's C2f model is replaced with the VoV-GSCSP module to reduce the model's computational requirements. Experiments show the improved YOLOv8 model achieves high overall performance. Compared to the original model, model parameters and GFLOPs are reduced by 52.66% and 19.9%, respectively, while mAP@0.5 is improved by 1%, recall by 2.7%, and precision by 2.4%. Further comparison with popular detection models YOLOv5 medium (YOLOv5m), YOLOv6 medium (YOLOv6m), and YOLOv8 medium (YOLOv8m) shows the improved model has the highest detection accuracy and lightest parameters for detecting four common tobacco pests, with optimal overall performance. The improved YOLOv8 detection model proposed facilitates precise, instantaneous pest detection and recognition for tobacco and other crops, securing high-accuracy, comprehensive pest identification.

Most pesticide spraying studies focus on increasing droplet deposition on target surfaces, with little consideration of post-deposition pesticide droplet absorption mechanisms. The pesticide droplet absorption efficiency of leaves is related to environmental factors and plant growth status during pesticide application. In field environments, irrigation-based soil moisture content regulation is a key plant growth status adjustment method. A theoretical pesticide droplet leaf absorption model was established to study the effects of soil moisture content on droplet absorption. Controlled environment experiments were carried out to investigate the absorption of pesticide droplets, and a chi-square test was used to verify the model reliability. The results showed that pesticide droplet leaf absorption flux first increased and then decreased with increasing soil water content. The diurnal leaf water potential and stomatal conductance variationsindicated a memory effect, resulting in diurnal variationsin the pesticide droplet leaf absorption flux. Under the same soil water conditions, the absorption flux first increased and then decreased from 8 am to 6 pm (Beijing Time). Effective application of pesticides can be significantly enhanced by controlling soil moisture content and selecting the appropriate spraying time. The best spraying combination improved the application effectiveness nearly threefold overthat of the worst combination. The results are crucial for rational pesticide use, crop spraying optimization, and water savings.

BackgroundsUAVs for crop protection hold significant potential for application in mountainous orchard areas in China. However, certain issues pertaining to UAV spraying need to be addressed for further technological advancement, aimed at enhancing crop protection efficiency and reducing pesticide usage. These challenges include the potential for droplet drift, limited capacity for pesticide solution. Consequently, efforts are required to overcome these limitations and optimize UAV spraying technology.MethodsIn order to balance high deposition and low drift in plant protection UAV spraying, this study proposes a plant protection UAV spraying method. In order to study the operational effects of this spraying method, this study conducted a UAV spray and grid impact test to investigate the effects of different operational parameters on droplet deposition and drift. Meanwhile, a spray model was constructed using machine learning techniques to predict the spraying effect of this method.Results and discussionThis study investigated the droplet deposition rate and downwind drift rate on three types of citrus trees: traditional densely planted trees, dwarf trees, and hedged trees, considering different particle sizes and UAV flight altitudes. Analyzing the effect of increasing the grid on droplet coverage and deposition density for different tree forms. The findings demonstrated a significantly improved droplet deposition rate on dwarf and hedged citrus trees compared to traditional densely planted trees and adopting a fixed-height grid increased droplet coverage and deposition density for both the densely planted and trellised citrus trees, but had the opposite effect on dwarfed citrus trees. When using the grid system. Among the factors examined, the height of the sampling point exhibited the greatest influence on the droplet deposition rate, whereas UAV flight height and droplet particle size had no significant impact. The distance in relation to wind direction had the most substantial effect on droplet drift rate. In terms of predicting droplet drift rate, the BP neural network performed inadequately with a coefficient of determination of 0.88. Conversely, REGRESS, ELM, and RBFNN yielded similar and notably superior results with a coefficient of determination greater than 0.95. Notably, ELM demonstrated the smallest root mean square error.

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency.

Field positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientific management for agricultural production. Compared to conventional methods, which often struggle with the complexities of field conditions and suffer from insufficient accuracy, this study employs a novel approach using self-developed multi-sensor array hardware as a portable field topographic surveying device. This innovative setup effectively navigates challenging field conditions to collect raw data. Data fusion is carried out using the Unscented Kalman Filter (UKF) algorithm. Building on this, this study combines the good point set and Opposition-based Differential Evolution for a joint improvement of the Slime Mould Algorithm. This is linked with the UKF algorithm to establish loss value feedback, realizing the adaptive parameter adjustment of the UKF algorithm. This reduces the workload of parameter setting and enhances the precision of data fusion. The improved algorithm optimizes parameters with an efficiency increase of 40.43%. Combining professional, mapping-grade total stations for accuracy comparison, the final test results show an absolute error of less than 0.3857 m, achieving decimeter-level precision in field positioning. This provides a new application technology for better implementation of agricultural digitalization.

This study examines the impact of canopy density, side wind speed, nozzle tilt angle, and droplet size on droplet penetration during plant protection spraying operations. Experiments conducted in citrus orchards evaluated how side wind speed and nozzle tilt angle influence droplet penetration across various canopy densities. A Phase Doppler Analyzer (PDA) was used to assess droplet size variations under different nozzle tilt angles and side wind speeds, yielding a multiple linear regression equation (R2 = 0.866) that links nozzle tilt angle and side wind speed with droplet size. Results showed that droplet size decreases with increasing nozzle tilt angle at a constant crosswind speed. Further experiments investigated the effects of droplet size and canopy leaf area density on droplet penetration, involving three canopy leaf area densities, four wind speeds, and six nozzle tilt angles. Droplet deposition and canopy coverage were measured under various spraying parameters, with conventional operations (0° nozzle tilt and orthogonal wind speeds) serving as controls. The study found that adjusting nozzle tilt angle and wind speed enhances droplet penetration in different canopy structures. Optimal parameters varied with leaf area density (LAD): an 18° tilt angle and 3 m/s wind speed for a LAD of 5.94 m3/m3, a 45° tilt angle and 2 m/s wind speed for a LAD of 8.47 m2/m3, and a 36° tilt angle and 3 m/s wind speed for a LAD of 11.12 m2/m3. At 1 m/s, droplet deposition followed a downward parabolic trend with changes in nozzle tilt angle, whereas at 2 m/s, deposition followed an upward parabolic trend. At a side wind speed of 3 m/s, droplet deposition remained unchanged with nozzle tilt angle but decreased with increasing canopy density. Nonlinear regression analysis indicated that leaf area density had a greater impact on deposition differences than droplet size, with droplet penetration decreasing as leaf area density increased. This study provides a reference for enhancing fog droplet penetration techniques in plant protection operations, offering practical guidelines for optimizing spraying conditions and improving pesticide use efficiency in different canopy structures.

The segmentation of surface defects in lithium batteries is crucial for enhancing the overall quality of the production process. However, the severe foreground–background imbalance in surface images of lithium batteries, along with the irregular shapes and random distribution of foreground regions, poses significant challenges for defect segmentation. Based on these observations, this paper focuses on the separation of foreground and background in surface defect images of lithium batteries and proposes a novel Mask Space Optimization Transformer (MSOFormer) for semantic segmentation of these images. Specifically, the Mask Boundary Loss (MBL) module in our model provides more efficient supervision during training to enhance the accuracy of the mask computation within the mask attention mechanism, thereby improving the model’s performance in separating foreground and background. Additionally, the Dynamic Spatial Query (DSQ) module allocates spatial information of the image to each query, enhancing the model’s sensitivity to the positions of small foreground targets in various scenes. The Efficient Pixel Decoder (EPD) ensures deformable receptive fields for irregularly shaped foregrounds while further improving the model’s performance and efficiency. Experimental results demonstrate that our method outperforms other state-of-the-art methods in terms of mean Intersection over Union (mIoU). Specifically, our approach achieves an mIoU of 84.18% on the lithium battery surface defect test set and 85.53% and 87.05% mIoUs on two publicly available defect test sets with similar defect characteristics to lithium batteries.

This study examined the characteristics of soil moisture movement and wetted body water distribution under drip irrigation conditions. The findings provided a crucial foundation for designing a precision drip irrigation control system for greenhouse tomatoes, enhancing control accuracy and conserving water. An indoor point source infiltration test was conducted to assess the impact of varying dripper flow rates and initial water content on soil moisture movement and wet body alterations. A mathematical model of wet front movement was established, and agricultural Internet of Things technology was utilized to design a precision drip irrigation control system for greenhouse tomatoes. This was then compared with a traditional drip irrigation control system. The results indicated that when wetted bodies reach the same wet depth, both irrigation time and total water consumption were influenced by flow rate and initial water content. Increasing the initial water content could effectively reduce both irrigation time and total water consumption. The Horizontal wetted front X value and the vertical wetted front Z value during the drip irrigation process exhibited a strong power function relationship with time, with R² exceeding 0.98. As irrigation time increases, the width-depth ratio of the wet body gradually transitioned from large to small, and increasing the initial water content aided in soil moisture vertical infiltration. After redistribution, the average water content in the wet body ranged between 68%𝜃𝐹𝐶 and 75%𝜃𝐹𝐶 , and the water uniformity coefficient exceeded 90%, aligning with normal growth conditions for tomatoes. The actual wet volume surpassed the planned wetted volume, and when the same initial water content was used, smaller total water consumption corresponded to smaller overflow volume. The target wetting depth is established based on the depth of tomato root distribution. When compared to the conventional drip irrigation control system, the precision drip irrigation control system developed in this study exhibited superior accuracy in wetting depth control and a more effective water-saving effect. The error in wetting depth during the flowering and fruit setting periods, as well as the fruiting period of the tomato, was reduced by 8.2% and 15.8% respectively, resulting in water savings of 16.2% and 22.6%.

This thesis introduces a UAV-based multispectral model, YOLO-SPAD, for rapid, non-destructive estimation of relative chlorophyll content of tea leaves. This is crucial for predicting growth conditions, implementing precise irrigation and fertilization, and increasing tea yields. The model leverages the spectral separation of the tea tree canopy and the YOLOv8 neural network architecture for image segmentation. Data from a five-channel multispectral camera on a drone was enhanced and used to train various neural network models. An improved YOLOv8 model, incorporating spectral fusion module, was proposed to better adapt to multispectral characteristics. This model achieved a segmentation MIoU of 91.61 for the tea tree canopy, outperforming other models. Different band combinations, vegetation indices, and texture features were analyzed to construct mapping transformation pairs for reflectance in the segmented area. This was applied to a SPAD prediction head to implement YOLO-SPAD. Experiments showed that the YOLO-SPAD model could predict tea tree canopy SPAD using UAV multispectral images, with an R2 of 0.85, RMSE of 2.14, and MAE of 1.79. This model supports dynamic monitoring of tea tree growth via UAV remote sensing, aiding crop nutrition improvement and precision agriculture implementation.

This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle direction and seven nozzle inclination levels (0°, 8°, 15°, 23°, 30°, 38°, 45°) to evaluate droplet distribution under different spraying parameters. A baseline condition with 0 m/s wind speed and a 0° nozzle angle served as the control. Utilizing Computational Fluid Dynamics (CFD) and regression analysis techniques in conjunction with field trials, the droplet penetration was analyzed. Results indicate that at constant wind speeds, adjusting the nozzle inclination angle against the direction of the side wind can significantly enhance droplet deposition in the canopy, with a 23° inclination providing the optimal increase in deposition volume, averaging a change of +16.705 μL/cm2. Multivariate nonlinear regression analysis revealed that both wind speed and nozzle inclination angle significantly affect the droplet penetration ratio, demonstrating a correlation between these factors, with wind speed exerting a greater impact than nozzle angle. Increasing the nozzle inclination angle at higher wind speeds improves the penetration ratio, with the optimal parameters being a 23° angle and 3 m/s wind speed, showing a 12.6% improvement over the control. The model fitted for the impact of nozzle angle and wind speed on droplet penetration was validated through field experiments, identifying optimal angles for enhancing penetration at wind speeds of 1, 2, and 3 m/s as 8°, 17°, and 25°, respectively. This research provides insights for improving droplet penetration techniques in plant protection operations.

To reduce the amount of pesticides in the environment, it is necessary to consider the wettability properties of pesticide droplets on the leaf surface to improve the spraying effect. The wettability properties of the droplet on the leaf surface are related not only to the properties of the liquid itself but also to the properties of the leaf surface. It is typically believed that leaf surface properties are difficult to control, and thus research has generally ignored this aspect of pesticide use. However, in the field environment, the structure and properties of the leaf surface can be altered by changing the moisture content of the soil where plants are grown. In this study, the roughness, contact angle, and surface free energy of the leaf surface were measured and calculated under different soil moisture contents to study the changes in the leaf surface wettability properties, with the aim of achieving efficient pesticide spraying by adjusting the soil water content. The results showed that the surface composition and microstructure of leaves were altered by the change in the soil moisture content, and the wettability properties of leaves decreased initially and then increased with a decrease in the soil moisture content. When the amount of soil water was sufficient or seriously insufficient, the wettability properties of the leaves were increased, but a lack of soil water may lead to irreversible damage to the plants. Therefore, before spraying pesticides on the leaf surfaces, the plants should be fully watered to improve the wettability properties of the leaf surface, which is conducive to the deposition and adhesion of pesticide droplets on the leaf surface and improved application effectiveness. The results of this study can provide a useful reference for the theoretical research and practices of precision spraying.

In this article, the inverse diffraction parabolic equation (IDPE) model based on the finite difference method is proposed, which is first applied in the multiple nonradiation targets orientation technology. In principle, the electromagnetic signal propagating in the transmission path will produce a reflected signal back to the source end while encountering the discontinuous objects. The distribution of the reflection or refraction intensity is directly associated with the distances and heights of the objects, so the location can be determined by means of analyzing the distribution. Here, according to the profile data of field intensity at the source end, the distribution of backward propagating electromagnetic waves are calculated rapidly by the IDPE. Then, the local extreme searching method is applied to search the coordinate of the convergence point of field intensity and the positions of multiple objects are finally determined. The piecewise linear function is used to model the irregular terrain. The influence of discontinuous terrain slopes on the false alarm probability of objects localization is also analyzed. The results show that the localization accuracy of the IDPE algorithm is affected by multiple factors, such as the radio frequency and sampling interval of field intensity. It is proved that the IDPE is a novel and efficient algorithm for multiple nonradiation targets orientation technology in long-range complicated terrain environment.

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