An efficient plant disease detection using transfer learning approach

TL;DR

This paper presents a transfer learning-based system using YOLOv7 and YOLOv8 models for accurate, scalable, and automated early detection of plant diseases from leaf images, improving crop management and sustainability.

Contribution

It introduces a novel application of fine-tuned YOLO models for plant disease detection, demonstrating superior performance over existing methods.

Findings

YOLOv8 achieved a mean Average Precision of 91.05.

The system effectively detects Bacteria, Fungi, and Viral diseases.

YOLOv8 outperforms other object detection methods in this context.

Abstract

Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.