FGA-Corn: an integrated system for precision pesticide application in center leaf areas using deep learning vision

In corn pest and disease prevention, traditional blanket pesticide spraying has led to significant pesticide waste and environmental pollution. To address this challenge, research into precision agricultural equipment based on computer vision has become a hotspot.

In this study, an integrated system named the FGA-Corn system is investigated for precision pesticide application, which consists of three important parts. The first part is the Front Camera Rear Funnel (FCRF) mechanical structure for efficient pesticide application. The second part is the Agri Spray Decision System (ASDS) algorithm, which is developed for post-processing the YOLO detection results, driving the funnel motor to enable precise pesticide delivery and facilitate real-time targeted application in specific crop areas. The third part is the GMA-YOLOv8 detection algorithm for center leaf areas. Building on the YOLOv8n framework, a more efficient GHG2S backbone generated by HGNetV2 enhanced with GhostConv and SimAM is proposed for feature extraction. The CM module integrated with Mixed Local Channel Attention is used for multi-scale feature fusion. An Auxiliary Head utilizing deep supervision is employed for improved assistive training.

Experimental results on both the D1 and D2 datasets demonstrate the effectiveness and generalization ability, with mAP@0.5 scores of 94.5% (+1.6%) and 90.1% (+1.8%), respectively. The system achieves a 23.3% reduction in model size and a computational complexity of 6.8 GFLOPs. Field experiments verify the effectiveness of the system, showing a detection accuracy of 91.3 ± 1.9% for center leaves, a pesticide delivery rate of 84.1 ± 3.3%, and a delivery precision of 92.2 ± 2.9%. This research not only achieves an efficient and accurate corn precision spraying program but also offers new insights and technological advances for intelligent agricultural machinery.