A deep learning-based leaf aphid detection approach using YOLOv8

Abstract

Aphids pose a serious threat to agricultural productivity due to their rapid reproduction and their role as plant virus vectors. Early manual detection is difficult due to the pests' microscopic size and tendency to hide under leaves. This study aims to develop an accurate and real-time aphid monitoring system using the YOLOv8 algorithm. The model was trained using four epoch scenarios (30, 50, 100, and 200) to identify the best configuration to address the challenges of small, overlapping objects and varying leaf backgrounds. The results showed that increasing the number of epochs positively correlated with model performance, with the 200-epoch scenario providing the most optimal results with 91.5% accuracy, 0.87 recall, 0.89 F1-score, and 0.915 mAP50. The model was then integrated into a smart monitoring dashboard that synchronizes visual detection results with IoT sensor data (temperature, humidity, and nutrients) in real time. This system not only validates the reliability of YOLOv8 under field conditions, but also provides an effective early warning system to support rapid decision-making in crop protection management.