ArticleFrontiers in plant science2024
Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Deep learning enables edge deployment for citrus leaf disease recognition in natural orchard scenes.Frontiers in plant science · 2026Article
- Areca palm yellow leaf disease (YLD) detection and severity grading from UAV Imagery with an enhanced YOLOv10s model.Frontiers in plant science · 2026Article
- Enhanced multiscale plant disease detection with the PYOLO model innovations.Scientific reports · 2025Article
- Advancements in Intelligent Sensing Technologies for Food Safety Detection.Research (Washington, D.C.) · 2025Review
- Rapid and accurate detection of peanut pod appearance quality based on lightweight and improved YOLOv5_SSE model.Frontiers in plant science · 2025Article
- GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds.Frontiers in plant science · 2025Article
- Artificial vision models for the identification of Mediterranean flora: An analysis in four ecosystems.PloS one · 2025Article
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7 authors.
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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 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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