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- Title
Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8.
- Authors
Uddin, Md. Sazid; Alam Mazumder, Md. Khairul; Prity, Afrina Jannat; Mridha, M. F.; Alfarhood, Sultan; Safran, Mejdl; Dunren Che
- Abstract
Cauliflower cultivation plays a pivotal role in the Indian Subcontinent's winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and handheld devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely 'Bacterial Soft Rot', 'Downey Mildew' and 'Black Rot' are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability
- Subjects
SOUTH Asia; SUSTAINABLE agriculture; CAULIFLOWER; AGRICULTURAL productivity; COMPUTER vision; AGRICULTURE; PRECISION farming; BLACKBERRIES
- Publication
Frontiers in Plant Science, 2024, p1
- ISSN
1664-462X
- Publication type
Article
- DOI
10.3389/fpls.2024.1373590