Spraying pesticides using row alignment in the maize seedling stage can effectively improve pesticide utilization and protect the ecological environment. Therefore, this study extracts a guidance line for maize crops using machine vision and develops an automatic row-oriented control system based on a high-clearance sprayer. First, the feature points of crop rows are extracted using a vertical projection method. Second, the candidate crop rows are obtained using a Hough transform, and two auxiliary line extraction methods for crop rows based on the slope feature outlier algorithm are proposed. Then, the guidance line of the crop rows is fitted using a tangent formula. To greatly improve the robustness of the vision algorithm, a Kalman filter is used to estimate and optimize the guidance line to obtain the guidance parameters. Finally, a visual row-oriented spraying platform based on autonomous navigation is built, and the row alignment accuracy and spraying performance are tested. The experimental results showed that, when autonomous navigation is turned on, the average algorithm time consumption of guidance line detection is 42 ms, the optimal recognition accuracy is 93.3%, the average deviation error of simulated crop rows is 3.2 cm and that of field crop rows is 4.36 cm. The test results meet the requirements of an automatic row-oriented control system, and it was found that the accuracy of row alignment decreased with increasing vehicle speed. The innovative spray performance test found that compared with the traditional spray, the inter-row pesticide savings were 20.4% and 11.4% overall, and the application performance was significantly improved.

Due to large line spacing and planting distances, the adoption of continuous and uniform pesticide spraying in vegetable farming can lead to pesticide waste, thus increasing cost and environmental pollution. In this paper, by applying deep learning and online identification methods, control technology for target-oriented spraying is studied with cabbages as the research object. To overcome motion blur and low average precision under strong light conditions during the operation of sprayers, an innovative YOLOV5 model implanted with a transformer module is utilized to achieve accurate online identification for cabbage fields under complex environments. Based on this concept, a new target-oriented spray system is built on an NVIDIA Jetson Xavier NX. Indoor test results show that the average precision is 96.14% and the image processing time is 51.07 ms. When motion blur occurs, the average precision for the target is 90.31%. Then, in a field experiment, when the light intensity is within the range of 3.76–12.34 wlx, the advance opening distance is less than 3.51 cm, the delay closing distance is less than 2.05 cm, and the average identification error for the cabbage diameter is less than 1.45 cm. The experimental results indicate that changes in light intensity have no significant impact on the identification effect. The average precision is 98.65%, and the savings rate reaches 54.04%. In general, the target-oriented spray system designed in this study achieves the expected experimental results and can provide technical support for field target spraying.

To enhance the automation and intelligence of trenching fertilization operations, this research proposes a real-time trunk detection model (Trunk-Seek) designed for spindle-shaped orchards. The model employs a customized data augmentation strategy and integrates the YOLO deep learning framework to effectively address visual challenges such as lighting variation, occlusion, and motion blur. Multiple object tracking algorithms were evaluated, and ByteTrack was selected for its superior performance in dynamic trunk tracking. In addition, a Positioning and Triggering Algorithm (PTA) was developed to enable precise localization and triggering for target-oriented fertilization. The system was deployed on an edge device, a test bench was established, and both laboratory and field experiments were conducted to validate its performance. Experimental results demonstrated that the detection model achieved an mAP50 of 98.9% and maintained a stable 32.53 FPS on the edge device, fulfilling real-time detection requirements. Test bench analysis revealed that variations in trunk diameter and operation speed affected triggering accuracy, with an average dynamic localization error of ±1.78 cm. An empirical model (T) was developed to describe the time-delay behavior associated with positioning errors. Field verification in orchards confirmed that Trunk-Seek achieved a triggering accuracy of 91.08%, representing a 24.08% improvement over conventional training methods. Combining high accuracy with robust real-time performance, Trunk-Seek and the proposed PTA provide essential technical support for the development of a visual target-oriented fertilization system in modern orchards.

Air-assisted spraying is the primary method of plant protection in orchards, and precision spraying according to the canopy characteristics of fruit trees can reduce waste and pollution due to pesticide drift. To facilitate targeted pesticide application in the canopy of fruit trees, this study employed a newly developed wind-speed-adjustable orchard sprayer and established a prediction model for deposition based on data from orthogonal trials using a central composite design accounting for the coupling effect of three-dimensional spatial parameters. The experimental design systematically quantified the interaction effects of spray distance (1.5–2.5 m), fan wind speed (10–20 m/s), and deposition height (0.5–3 m) on the spatial distribution of droplets. Model significance was p < 0.0001 and the misfit term was significant (p = 0.2193), supporting its validity. The research found that wind speed and distance significantly interact in influencing deposition. By adjusting fan speed and spray distance, variable applications can be achieved in different canopy zones during plant protection operations. The response surface model developed in this study can be applied to variable-rate spraying control systems, thus providing a quantitative basis for dynamic droplet control guided by canopy characteristics. Validation tests revealed that the model’s accuracy was lower in high canopy regions and upwind spraying scenarios, indicating areas for further research.

View-dependent canopy gap fraction is a directional descriptor of canopy openness that provides structural information for characterizing fruit-tree canopy heterogeneity. However, existing LiDAR-based canopy characterization methods are limited in their stability for characterizing zone-specific variations in canopy occlusion. This study proposes a LiDAR point-cloud column projection method for estimating zone-specific view-dependent canopy gap fraction of fruit tree canopies. A flash LiDAR sensor and an RGB camera were used to acquire paired point-cloud and image data from a simulated fruit tree and real peach trees under matched viewing directions and consistent region-of-interest constraints. Image-derived canopy gap fraction, calculated from the blue-cloth background area in each 3 × 3 subregion, was used as the reference value. The proposed column projection method was compared with a regional volumetric occupancy method and a regional two-dimensional projection coverage method. Under the indoor method-development conditions, using 88 paired frames, the proposed method achieved a mean absolute error of 8.59%, a Pearson correlation coefficient of 0.925, and a Spearman rank correlation coefficient of 0.914 relative to the image-derived reference values. In the preliminary semi-controlled outdoor evaluation using eight paired real-tree samples, the method achieved a mean absolute error of 10.48% and showed lower numerical errors and systematic bias than the two baseline methods. These results indicate that the proposed method can provide zone-specific view-dependent canopy gap fraction estimates that are more consistent with image-derived reference values under the tested conditions.