LDTC-YOLO: A Lightweight Detection Model for Typical Citrus Leaf and Fruit Diseases in Real Orchard Environments

Abstract

Accurate detection of citrus leaf and fruit diseases is important for precision orchard management. However, real orchard images often contain small disease symptoms, leaf and fruit overlap, illumination variation, and cluttered backgrounds, making reliable detection challenging. This study proposes LDTC-YOLO, a lightweight YOLOv8n-based detection model for typical citrus leaf and fruit diseases in real orchard environments. To improve detection accuracy and model compactness, LDTC-YOLO integrates an Adaptive Feature Pyramid Network (AFPN) for cross-level feature fusion, Coordinate Attention (CA) for disease-region feature enhancement, a Lightweight Shared Convolutional Detection (LSCD) head for reducing parameter redundancy, and Wise-IoU (WIoU) for bounding-box regression optimization. In addition, a self-collected handheld citrus disease dataset, HOCD-4, was constructed using close-range smartphone images captured in real orchards. The dataset covers leaf and fruit symptoms of four typical citrus diseases: Huanglongbing/citrus greening (HLB), black spot, canker, and melanose. Experimental results show that LDTC-YOLO achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 values of 0.915, 0.843, 0.894, and 0.648, respectively. Compared with YOLOv8n, LDTC-YOLO reduced parameters, GFLOPs, and model size from 3.006 M to 1.887 M, 8.1 to 7.4, and 5.97 MB to 3.83 MB, while increasing inference speed from 43.14 FPS to 47.45 FPS. These results indicate that LDTC-YOLO improves detection performance while maintaining a compact and efficient model profile, providing a potential reference for citrus disease detection under real orchard imaging conditions.

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