Bean Leaf Disease Detection Using Yolov8 Segmentation in Deep Learning
Year: 2024
Type: article
Abstract: This research presents a deep learning-based approach for accurate and efficient detection of bean leaf diseases. Leveraging the YOLOv8 instance segmentation architecture, we fine-tuned the model on a curated dataset of bean leaf images. The model effectively detects and segments three primary bean leaf diseases: angular leaf spot, bean rust, and healthy leaves. Experimental results demonstrate moderate performance, achieving accuracies of 68.63% and 74.49% for original and augmented datasets, respectively. Notably, the model's confidence levels, as measured by precision, recall, F1- score, and mean average precision (MAP), improved significantly after incorporating augmented data. These results underscore the effectiveness of our approach in providing farmers with a reliable tool for monitoring crop health and implementing targeted disease management strategies.
DOI: 10.20372/nadre:10520
Author: ALEBACHEW MEKURIAW DAGNAW
Institution: Woldia University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6888513631
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