Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8
Abstrak
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
Topik & Kata Kunci
Penulis (10)
Md. Sazid Uddin
M. Khairul
Alam Mazumder
Afrina Jannat Prity
M. Mridha
Mejdl S. Safran
Dunren Che
Hao Lu
Jana Sha fi
N. Mazumder
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 30×
- Sumber Database
- Semantic Scholar
- DOI
- 10.3389/fpls.2024.1373590
- Akses
- Open Access ✓