ArticleBMC plant biology2025
Deep learning method for cucumber disease detection in complex environments for new agricultural productivity.
Article in BMC plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- YOLOv8n-DSLW: A Deployment-Oriented AI-Enabled Vision-Sensing Model for Tiny Strawberry Disease and Pest Detection in Greenhouse Images.Sensors (Basel, Switzerland) · 2026Article
- From detection to action: artificial intelligence in integrated pest and invasive plant management.Advanced biotechnology · 2026Review
- A lightweight graph-enhanced deep learning framework for explainable cucumber leaf disease diagnosis.Scientific reports · 2026Article
- Deep learning-based seed germination prediction using morphological traits and RGB images.BMC plant biology · 2026Article
- Lightweight architecture optimization of YOLOv12n for improved cotton verticillium wilt detection.Frontiers in plant science · 2026Article
- WMC-Leafset: A dataset of wax gourd and Mangalore cucumber plants for leaf miner and pest infestation diseased object detection.Data in brief · 2025Article
- Lightweight detection of grapevine downy mildew using UAV based on image information enhancement and adaptive dual-path fusion attention.BMC plant biology · 2025Article
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Authors and funding
5 authors.
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Abstract
Cucumber disease detection under complex agricultural conditions faces significant challenges due to multi-scale variation, background clutter, and hardware limitations. This study proposes YOLO-Cucumber, an improved lightweight detection algorithm based on YOLOv11n, incorporating four key innovations: (1) Deformable Convolutional Networks (DCN) for enhanced feature extraction of irregular targets, (2) a P2 prediction layer for fine-grained detection of early-stage lesions, (3) a Target-aware Loss (TAL) function addressing class imbalance, and (4) Channel Pruning via Batch Normalization (CPBN) for model compression. Experiments on our cucumber disease dataset demonstrate that YOLO-Cucumber achieves a 6.5% improvement in mAP@50 (93.8%), while reducing model size by 3.87 MB and increasing inference speed to 218 FPS. The model effectively handles symptom variability and complex detection scenarios, outperforming mainstream detection algorithms in accuracy, speed, and compactness, making it ideal for embedded agricultural applications.
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