Cross-Scale Unified Semantic Space Learning for Small-Scale Pest and Disease Detection in Protected Agriculture

In protected agriculture such as greenhouses, pest and disease monitoring via UAVs and fixed cameras suffers from extremely small object proportions owing to shooting altitude constraints, posing considerable detection challenges. Moreover, fine-grained annotation of numerous small-scale images incurs prohibitive costs. Targeting this bottleneck, this paper proposes a cross-scale unified semantic space learning framework and introduces an end-to-end DS-DETR detector based on DETR. Unlike existing methods relying on domain adaptation, multi-scale fusion, or super-resolution reconstruction, this work explicitly models instance-level cross-scale semantic correlation, transferring fine-grained semantics from large-scale close-up images to small-scale scene feature space. A Single-Point Dual-Shooting (SPDS) strategy is adopted to collect high-fidelity paired images via ordinary smartphones at low cost. A dual-stream encoder with cross-view attention and an instance-level contrastive loss align features of identical instances in a unified semantic space. A self-built CropScale-Det dataset covering three crop diseases is constructed in greenhouse scenarios. Experimental results show that DS-DETR achieves 42.5 ± 1.2% mAP@50 under limited annotations, outperforming YOLOv8-n by 11.2%, with small-target average precision reaching 26.8 ± 1.1%. Ablation experiments and feature visualization validate the effectiveness of the designed mechanism. This approach considerably reduces reliance on large-scale densely annotated data, establishing a data-efficient proof-of-concept for small-scale pest detection in protected agriculture.