CoMATE-DETR: Coordinated Model Compression and Matching-Aware Training for Efficient Citrus Leaf Disease Detection

Accurate citrus leaf disease detection requires a balance between visual discrimination and computational efficiency for camera-based monitoring in digital agriculture. This study proposes CoMATE-DETR, an efficient RT-DETR-based detector that coordinates model compression with matching-aware training. The framework integrates a partial-convolution backbone, external attention, a Dense O2O Matching Strategy (DOMS), and Matchability-Aware Loss (MAL) without adding inference branches. Experiments used five citrus leaf categories. Across training seeds 0, 1, and 2, the complete model achieved 91.70 ± 0.41% mAP@50:95 on the fixed 566-entry test evaluation, compared with 91.79 ± 0.41% for the compatible baseline. The reported architecture profile contains 9.7 M parameters and 17.6 GFLOPs, reductions of 51.7% and 41.7% from the baseline, with a throughput of 106 FPS on an NVIDIA GeForce RTX 4090. Factorial ablations increased mAP@50:95 from 90.56% for PConv + EA to 91.70% for the complete training configuration. PConv + EA + MAL achieved the highest ablation mean of 92.94 ± 0.89%. These results establish a compact detector with high detection accuracy and quantify the distinct effects of DOMS and MAL on the accuracy–efficiency balance. On the external BDLemonLeaf V6 Greening subset, the frozen checkpoint achieved 63.05% AP50:95, 95.32% AP50, and 77.67% AP75.