智慧农业（中英文）2026，Vol.8Issue(4)：204-216,13.DOI:10.12133/j.smartag.SA202601027
多尺度异构特征协同的棉花叶片病害检测模型
Multi-Scale Heterogeneous Feature Synergistic Model for Cotton Leaf Disease Detection
摘要
Abstract
[Objective]Detecting cotton leaf diseases in natural field environments is challenging due to diverse image interferences,variable leaf-spot̂ sizes,and t̂he demand for fast̂ comput̂at̂ion on mobile phones and ot̂her 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(Mult̂i-Scale Het̂erogeneous Synergist̂ic Feat̂ure DE-TR),an improved det̂ect̂ion model based on t̂he RT-DETR framework,is proposed.It̂ aims t̂o achieve high-precision,low-power diag-nosis 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 construct-ed 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 spa-tial 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 compe-tition 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 ra-tios,so that deep semantic features were aligned precisely with shallow geometry.Moreover,to solve the problem of artifacts appear-ing 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 inte-grated reflection padding and partial convolution techniques,the model successfully curtailed invalid edge noise and diminished com-putational redundancy without sacrificing the geometric continuity of features.[Results and Discussions]Empirical benchmarks demon-strated 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 preci-sion(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 YO-LOv8m 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 effi-cient solution for resource-constrained object detection tasks.[Conclusions]MHSF-DETR addresses t̂he long-st̂anding accuracy-efficiency t̂rade-off in cot̂t̂on disease monit̂oring.By integrating hierarchical perception,adaptive fusion and edge refine-ment,t̂he model mit̂igat̂es scale disparit̂y and resource-const̂raint̂ challenges.It̂ provides a feasible light̂weight̂ t̂emplat̂e for real-t̂ime diagnosis on agricult̂ural edge devices t̂o support̂ deployment̂ in smart̂-farming syst̂ems.Future work will expand validation to other plant organs(bolls,stems)and conduct̂ rigorous embedded-hardware field t̂est̂s t̂o assess real-world robust̂ness.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Key words
cotton disease detection/RT-DETR/lightweight model/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
沈学利,张越,金海波,张旭旭..多尺度异构特征协同的棉花叶片病害检测模型[J].智慧农业（中英文）,2026,8(4):204-216,13.基金项目
国家自然科学基金项目(62173171) National Natural Science Foundation of China(62173171) （62173171）
