An efficient plant disease detection using transfer learning approach
Journal:
arXiv
Published Date:
Jun 28, 2025
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.
