CLH-DETR: An Enhanced and Lightweight RT-DETR for Mulberry Disease Detection in Natural Orchard Environments

Mulberry (Morus alba L.) is an important perennial woody crop for sericulture, medicinal resource development, and ecological conservation. Accurate disease and pest detection in natural mulberry orchards remains challenging due to dense foliage, severe occlusion, varying illumination, and the presence of small lesions with weak texture features. To address these problems, this study proposes CLH-DETR, a lightweight detection framework improved from the Real-Time Detection Transformer (RT-DETR) for mulberry disease and pest detection under natural field conditions. The proposed model introduces four targeted improvements: Cross-Stage Partial Progressive Multi-Scale Feature Aggregation (CSP-PMSFA) is designed to strengthen multi-scale lesion feature extraction while reducing redundant computation, the Lesion Detail Enhancement Block (LDEB) is designed to enhance weak lesion details and responses related to lesion boundaries, Haar Wavelet Downsampling (HWD) is adopted to preserve texture and structural information during feature downsampling, and Focaler-ShapeIoU is introduced to improve bounding-box regression for irregular disease regions. Experiments on the Mulberry Disease Dataset show that CLH-DETR achieves 80.1% precision, 75.3% mAP50, and 55.9% mAP50:95, improving the RT-DETR baseline by 3.9, 2.3, and 2.2 percentage points, respectively. Meanwhile, the number of parameters decreases from 19.9 M to 13.4 M, and the computational cost is reduced from 57.1 G to 46.4 G FLOPs. Compared with representative YOLO- and DETR-based detectors, CLH-DETR provides a favorable balance between detection accuracy and model complexity. When deployed on an iPhone 16 Pro using CoreML with FP16 precision, the model achieved an average latency of 16.5 ms per image, corresponding to 60.6 FPS. This result indicates its potential for edge-assisted mulberry disease inspection under the tested hardware conditions.