ArticleFrontiers in plant science2025
YOLO-ODD: an improved YOLOv8s model for onion foliar disease detection.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Research on Lightweight Rose Disease Detection Based on Transferable Feature Representation.Plants (Basel, Switzerland) · 2026Article
- YOLO-based deep learning framework for real-time multi-class plant health monitoring in precision agriculture.Scientific reports · 2026Article
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
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10 authors.
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Abstract
Onion crops are affected by many diseases at different stages of growth, resulting in significant yield loss. The early detection of diseases helps in the timely incorporation of management practices, thereby reducing yield losses. However, the manual identification of plant diseases requires considerable effort and is prone to mistakes. Thus, adopting cutting-edge technologies such as machine learning (ML) and deep learning (DL) can help overcome these difficulties by enabling the early detection of plant diseases. This study presents a cross layer integration of YOLOv8 architecture for detection of onion leaf diseases
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