Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8
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. Thi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1373590/full |
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| author | Md. Sazid Uddin Md. Khairul Alam Mazumder Afrina Jannat Prity M. F. Mridha Sultan Alfarhood Mejdl Safran Dunren Che |
| author_facet | Md. Sazid Uddin Md. Khairul Alam Mazumder Afrina Jannat Prity M. F. Mridha Sultan Alfarhood Mejdl Safran Dunren Che |
| author_sort | Md. Sazid Uddin |
| collection | DOAJ |
| description | 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 hand-held 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 |
| first_indexed | 2024-04-24T07:58:37Z |
| format | Article |
| id | doaj.art-c4450472dbf14a6fb70b43c5d9f35566 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-24T07:58:37Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-c4450472dbf14a6fb70b43c5d9f355662024-04-18T04:29:30ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-04-011510.3389/fpls.2024.13735901373590Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8Md. Sazid Uddin0Md. Khairul Alam Mazumder1Afrina Jannat Prity2M. F. Mridha3Sultan Alfarhood4Mejdl Safran5Dunren Che6Department of Computer Science, American International University-Bangladesh, Dhaka, BangladeshDepartment of Computer Science, American International University-Bangladesh, Dhaka, BangladeshDepartment of Computer Science, American International University-Bangladesh, Dhaka, BangladeshDepartment of Computer Science, American International University-Bangladesh, Dhaka, BangladeshDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaSchool of Computing, Southern Illinois University, Carbondale, IL, United StatesCauliflower 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 hand-held 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 sustainabilityhttps://www.frontiersin.org/articles/10.3389/fpls.2024.1373590/fullcauliflower disease detectionmachine visionYOLOv8agricultural disease managementvegetable disease detection |
| spellingShingle | Md. Sazid Uddin Md. Khairul Alam Mazumder Afrina Jannat Prity M. F. Mridha Sultan Alfarhood Mejdl Safran Dunren Che Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8 Frontiers in Plant Science cauliflower disease detection machine vision YOLOv8 agricultural disease management vegetable disease detection |
| title | Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8 |
| title_full | Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8 |
| title_fullStr | Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8 |
| title_full_unstemmed | Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8 |
| title_short | Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8 |
| title_sort | cauli det enhancing cauliflower disease detection with modified yolov8 |
| topic | cauliflower disease detection machine vision YOLOv8 agricultural disease management vegetable disease detection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1373590/full |
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