Frontiers in Plant Science (Apr 2024)

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

  • Md. Sazid Uddin,
  • Md. Khairul Alam Mazumder,
  • Afrina Jannat Prity,
  • M. F. Mridha,
  • Sultan Alfarhood,
  • Mejdl Safran,
  • Dunren Che

DOI
https://doi.org/10.3389/fpls.2024.1373590
Journal volume & issue
Vol. 15

Abstract

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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

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