关键词: 棉花病害检测, RT-DETR, 轻量化模型, 注意力机制, 特征融合

Abstract:

[Objective] Detecting cotton leaf diseases in natural field environments is challenging due to diverse image interferences, variable leaf‑spot sizes, and the demand for fast computation on mobile phones and other small‑scale devices. Nevertheless, existing lightweight models cannot well balance detection accuracy and computational efficiency, especially for detecting small lesions and suppressing noise surrounding leaves. To address these challenges, MHSF‑DETR (Multi‑Scale Heterogeneous Synergistic Feature DETR), an improved detection model based on the RT‑DETR framework, is proposed. It aims to achieve high‑precision, low‑power diagnosis in complex agricultural scenarios. [Methods] The primary innovation of this study consisted of the complete reconfiguration of the feature extraction and fusion architectures. Firstly, a hierarchical context-selective perception network (HCSP-Net) was constructed as the backbone to replace conventional architectures. This backbone employed a differentiated processing strategy tailored to the depth of the feature maps: In the early parts of the process where the features were simple, it used something called micro-macro spatial context attention (M²-SCA). This module used a channel semantic filter then a dual stream spatial perception structure to actively capture high frequency textures of micro-lesions and preserve macro semantics so that fine details were not lost when downsampled. At the deep feature stage, a competitive selection fusion (CSF) module was added. Unlike the traditional static summing approach, CSF created a dynamic competition arbitration system that flexibly balances local importance versus overall coherence via soft competition gates, making the semantics sharper and filtering away irrelevant background noise. Secondly, to tackle the spatial and semantic misalignment that was commonly seen in cross-level feature fusions, a learnable weighted context fusion (LWC-Fusion) module was created inside the neck network. This module used global amplitude dynamic weighting to learn autonomously the best blending ratios, so that deep semantic features were aligned precisely with shallow geometry. Moreover, to solve the problem of artifacts appearing at irregular leaf boundaries caused by traditional zero-padding convolutions, an edge-aware reconstruction mechanism (EARM) was proposed. By using edge-refined convolution (ER-Conv) and the edge-refined convolution C3 module (ER-ConvC3), which integrated reflection padding and partial convolution techniques, the model successfully curtailed invalid edge noise and diminished computational redundancy without sacrificing the geometric continuity of features. [Results and Discussions] Empirical benchmarks demonstrated that the proposed MHSF-DETR achieved a superior balance between detection performance and computational efficiency. Compared to the RT-DETR-R18 baseline, MHSF-DETR yielded a significant 3.2 percentage points increase in mean average precision (mAP), while simultaneously reducing parameters by 22.42% and GFLOPs by 13.29%. When benchmarked against mainstream detectors, MHSF-DETR consistently outperformed models such as YOLOv5m, YOLOv10m, and RT-DETR-R50. Although YOLOv8m maintained a marginal mAP50 lead in specific scenarios, its exorbitant computational overhead rendered it less practical for real-time deployment compared to MHSF-DETR. It successfully matched the lean efficiency of YOLOv10m but excelled in detection accuracy. Furthermore, extensive ablation studies confirmed that these performance gains stemmed from the structural synergy among the HCSP-Net backbone, the LWC-Fusion neck, and specialized reconstruction modules, rather than isolated component upgrades. These results validated the effectiveness of MHSF-DETR design in optimizing feature extraction and fusion, offering a highly efficient solution for resource-constrained object detection tasks. [Conclusions] MHSF‑DETR addresses the long‑standing accuracy‑efficiency trade‑off in cotton disease monitoring. By integrating hierarchical perception, adaptive fusion and edge refinement, the model mitigates scale disparity and resource‑constraint challenges. It provides a feasible lightweight template for real‑time diagnosis on agricultural edge devices to support deployment in smart‑farming systems. Future work will expand validation to other plant organs (bolls, stems) and conduct rigorous embedded‑hardware field tests to assess real‑world robustness.

Key words: cotton disease detection, RT-DETR, lightweight model, attention mechanism, feature fusion