YOLOv8-GDCI: Research on the Phytophthora Blight Detection Method of Different Parts of Chili Based on Improved YOLOv8 Model

Abstract

Smart farms are crucial in modern agriculture, but current object detection algorithms cannot detect chili Phytophthora blight accurately. To solve this, we introduced the YOLOv8-GDCI model, which can detect the disease on leaves, fruits, and stem bifurcations. The model uses RepGFPN for feature fusion, Dysample upsampling for accuracy, CA attention for feature capture, and Inner-MPDIoU loss for small object detection. In addition, we also created a dataset of chili Phytophthora blight on leaves, fruits, and stem bifurcations, and conducted comparative experiments. The results manifest that the YOLOv8-GDCI model demonstrates outstanding performance across a gamut of comprehensive indicators. In comparison with the YOLOv8n model, the YOLOv8-GDCI model demonstrates an improvement of 0.9% in precision, an increase of 1.8% in recall, and a remarkable enhancement of 1.7% in average precision. Although the FPS decreases slightly, it still exceeds the industry standard for real-time object detection (FPS > 60), thus meeting the requirements for real-time detection.