Development of an Agent Based Intelligent System for Early Detection of Onion Disease Using Deep Learning

Abstract

Onion cultivation is essential for food security and income generation, especially among smallholder farmers. However, production is severely affected by fungal and bacterial diseases that reduce both yield and quality. Traditional detection methods rely on manual inspection, which is often slow, subjective, and prone to human error. Although artificial intelligence has improved automated image-based detection, many existing systems focus only on visible symptoms and fail to integrate intelligent decision-making mechanisms. To address this gap, this study developed an Agent-Based Intelli-gent System for onion disease detection, integrating the YOLOv8 deep learning object detection model within a multi-agent framework. The model was implemented using Python and the Ultralytics YOLOv8 framework on Google Colab. Experimental results demonstrated outstanding performance, achieving a precision of 0.998, recall of 1.000, and mAP@0.5 of 0.995 on unseen test data, with only one misclassification observed. The system significantly outperformed benchmark models, confirming its robustness and generalization capability