AN EFFICIENT RICE PEST DETECTION METHOD BASED ON YOLOV11N
TL;DR
This study presents a lightweight YOLOv11n-based framework for accurate edge deployment that integrates a Multi-cognitive Visual Adapter (Mona), a Detail-Preserving Contextual Fusion module (DPCF), a Detail-Preserving Contextual Fusion module (DPCF) and Wise-IoU-based non-maximum suppression (Wise-IoU-NMS).
Abstract
Accurate and efficient rice-pest detection is essential for field scouting, early warning and precision control. This study presents a lightweight YOLOv11n-based framework for accurate edge deployment. It integrates a Multi-cognitive Visual Adapter (Mona), a Detail-Preserving Contextual Fusion module (DPCF) and Wise-IoU-based non-maximum suppression (Wise-IoU-NMS). Mona improves multi-scale feature representation with limited overhead; DPCF preserves fine-grained pest details during contextual fusion; and Wise-IoU-NMS stabilizes localization of overlapping and densely distributed targets. On a public dataset of 5,212 images from six near-balanced rice-pest classes, divided 8:1:1 for training, validation and testing, the proposed model achieved 95.4% mAP@50, 94.8% precision and 94.7% recall with 2.65 M parameters and 6.5 GFLOPs. The F1-confidence curve identified 0.476 as the alarm threshold. Deployment on Jetson Nano using ONNX and frame-level logging demonstrated a practical workflow for real-time monitoring and selective spraying.