Deep learning method for cucumber disease detection in complex environments for new agricultural productivity.

Journal: BMC plant biology
Published Date: Jul 7, 2025

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.

Authors

  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • Xuewei Wang
    Image Center Department, Affiliated Cancer Hospital of Harbin Medical University, 150 Haping Road, Nangang District, Harbin, Heilongjiang Province, PR China.
  • Qian Chen
    Department of Pain Medicine Guizhou Provincial Orthopedics Hospital Guiyang Guizhou China.
  • Peng Yan
    Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Room 603, No. 6 Tiantan Xili, Dongcheng District, Beijing, China.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.

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