Optimization and Experimental Evaluation of a Deep Learning-Based Target Spraying Device for Weed Control in Soybean Fields

Weed management during the seedling stage is a critical component of soybean production. Efficient weed control can significantly improve crop yield and crop quality. However, conventional spraying techniques exhibit low pesticide utilization and contribute to environmental pollution. To address these challenges, this study proposes a deep learning-based precision target spraying method. A lightweight YOLOv5-MobileNetv3-SE model was developed by replacing the backbone feature extraction network and incorporating an attention mechanism. Field images of weeds were collected to construct a dedicated dataset, and the detection performance of the model was evaluated. Furthermore, a grid-based matching spraying algorithm was developed to synchronize target detection with spray actuation. The system time delay, including image processing delay, communication and control delay, and spray deposition delay, was analyzed and measured, and a time-delay compensation strategy was implemented to ensure accurate spraying. Experimental results demonstrated that the improved model achieved an mAP@0.5 of 86.9%, a model size of 7.5 MB, and a frame rate of 38.17 frames per second. The weed detection accuracy exceeded 92.94%, and spraying accuracy exceeded 85.88% at forward speeds of 1–4 km·h−1. Compared with conventional continuous spraying, the proposed method achieved pesticide reduction rates of 79.0%, 72.5%, 55.8%, and 48.6% at weed coverage rates of 5%, 10%, 15%, and 20%, respectively. The proposed method provides a practical approach for precise herbicide application, effectively reducing chemical usage and minimizing environmental impact.