Abstract: Nowadays, pest infestations cause significant reductions in agricultural productivity all over the world. To control pests, farmers often apply excessive volumes of pesticides due to the difficulty of manually detecting the pest at an early stage. Their overuse of pesticides has led to environmental pollution and health risks. To address these challenges, many novel systems have been developed to identify pests early, allowing farmers to be alerted about the exact location where pests are detected. However, these systems are constrained by their lack of real-time detection capabilities, limited mobile integration, ability to detect only a small number of pest classes, and the absence of a web-based monitoring system. This paper introduces a pest detection system that leverages the lightweight YOLO deep learning framework and is integrated with a web-based monitoring platform. The YOLO object detection architectures, including YOLOv8n, YOLOv9t, and YOLOv10-N, were studied and optimized for pest detection on smartphones. The models were trained and validated using merging publicly datasets containing 29 pest classes. Among them, the YOLOv9t achieves top performance with a mAP@0.5 value of 89.8%, precision of 87.4%, recall of 84.4%, and an inference time of 250.6ms. The web-based monitoring system enables dynamic real-time monitoring by providing farmers with instant updates and actionable insights for effective and sustainable pest management. From there, farmers can take necessary actions immediately to mitigate pest damage, reduce pesticide overuse, and promote sustainable agricultural practices.

Wong Min On and Nirase Fathima Abubacker, “YOLO-Driven Lightweight Mobile Real-Time Pest Detection and Web-Based Monitoring for Sustainable Agriculture” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151267

@article{On2024,
title = {YOLO-Driven Lightweight Mobile Real-Time Pest Detection and Web-Based Monitoring for Sustainable Agriculture},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151267},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151267},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Wong Min On and Nirase Fathima Abubacker}
}