Citation

Zaman Abid, Md. Shahriar and Jahan, Busrat and Al Mamun, Abdullah Sarwar and Hossen, Md. Jakir and Hossain Mazumder, Shazzad (2024) Bangladeshi crops leaf disease detection using YOLOv8. Heliyon, 10 (18). e36694. ISSN 2405-8440

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Abstract

The agricultural sector in Bangladesh is a cornerstone of the nation’s economy, with key crops such as rice, corn, wheat, potato, and tomato playing vital roles. However, these crops are highly vulnerable to various leaf diseases, which pose significant threats to crop yields and food security if not promptly addressed. Consequently, there is an urgent need for an automated system that can accurately identify and categorize leaf diseases, enabling early intervention and management. This study explores the efficacy of the latest state-of-the-art object detection model, YOLOv8 (You Only Look Once), in surpassing previous models for the automated detection and categorization of leaf diseases in these five major crops. By leveraging modern computer vision techniques, the goal is to enhance the efficiency of disease detection and management. A dataset comprising 19 classes, each with 150 images, totaling 2850 images, was meticulously curated and annotated for training and evaluation. The YOLOv8 framework, known for its capability to detect multiple objects simultaneously, was employed to train a deep neural network. The system’s performance was evaluated using standard metrics such as mean Average Precision (mAP) and F1 score. The findings demonstrate that the YOLOv8 framework successfully identifies leaf diseases, achieving a high mAP of 98% and an F1 score of 97%. These results underscore the significant potential of this approach to enhance crop disease management, thereby improving food security and promoting agricultural sustainability in Bangladesh.

Item Type:	Article
Uncontrolled Keywords:	Technology in agriculture
Subjects:	S Agriculture > S Agriculture (General) T Technology > TJ Mechanical Engineering and Machinery > TJ163.26-163.5 Energy conservation
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 01:32
Last Modified:	01 Oct 2024 01:32
URII:	http://shdl.mmu.edu.my/id/eprint/13003

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