Optimized YOLOv8 for Insect Detection in Agricultural Farms Using Edge Vision Systems

Abstract

Farmers started to adopt smart gadgets like automated pest detection and kiosks for their work in light of the AI revolution in agriculture. These kinds of technologies help farmers identify pest infestations early, which allows the farmers to take action accordingly and prevent crop loss. This results in improvement in both yield and sustainability. However, running an advanced model such as YOLOv7 on low-power edge devices remains a challenge, especially in real time. To address the problem, our research brings an innovative Advanced Edge-Based Vision System designed for detecting small insects and classification in agricultural environments. The model built utilizes the Jetson Nano platform, TensorRT optimization, and an enhanced YOLOv8 model. To optimize the performance, the model uses compression techniques, such as INT8 quantization, and is accelerated by TensorRT. The YOLOv8 model is trained on a specialised insect dataset, and this optimization technique results in a tenfold reduction in memory usage, yet performs efficiently and also improves the inference speed using Jetson Nano by achieving a processing rate of 45 Frames Per Second (FPS). Despite these optimizations and memory reduction, the model serves at its best where the F1-scores exceeding 95% ensure accurate detection of the pest. This study demonstrates the successful deployment of lightweight, high-performance vision models on edge devices, enabling real-time, accurate pest detection. The findings contribute to advancing precision agriculture by making intelligent pest management more accessible and efficient.