PEFF-Net: A Lightweight Pest Edge Feature Fusion Network for Real-Time Rice Pest Detection Towards Edge Deployment

Accurate and efficient rice pest detection is paramount for ensuring food security and enhancing agricultural production efficiency. Traditional manual pest monitoring methods fall short of meeting the precision and efficiency demands of modern agriculture. To address the challenge of deploying high-precision object detection models on resource-constrained edge devices, we propose an efficient, lightweight rice pest detection model, termed PestEdgeFeatureFusion-Net (PEFF-Net), and implement a comprehensive edge-side offline intelligent monitoring system. PEFF-Net integrates Edge Feature Extraction Stem (EFStem), the Edge Semantic Fusion Module (ESF), and the Lightweight Cross-layer Feature Fusion Output Module (LCFO). By streamlining deep feature maps and strengthening edge feature perception, the model significantly reduces parameter overhead while enhancing multi-scale feature fusion capabilities. Experimental results demonstrate that on the Z-RP12 dataset containing 5000 images, PEFF-Net has 2.12 M parameters and achieves a mAP0.5 of 90.6%, providing a favorable balance between detection accuracy and model compactness. We employ the Jetson Orin Nano Super 8 GB as the core hardware platform and leverage TensorRT for INT8 quantization acceleration. The optimized model achieves 31 FPS with a mean latency of approximately 32.1 ms on the Jetson Orin Nano Super 8 GB. An independent cross-camera field evaluation further supports the feasibility of the proposed edge-side detection system under the tested conditions.