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.
Zheng et al. (2026) studied this question.