农业机械学报2026，Vol.57Issue(18)：81-92,12.DOI:10.6041/j.issn.1000-1298.2026.18.008
基于冠层图像煤污识别的温室白粉虱危害分级方法
Method for Greenhouse Whitefly Damage Grading Based on Sooty Mold Recognition in Canopy Images
摘要
Abstract
Aiming to address the cross-scene generalization deficit of end-to-end CNN classifiers on the task of greenhouse whitefly damage grading in facility vegetable production,where standard models tended to fix on background-correlated cues such as illumination and plant growth stage rather than the physical signature of sooty mold,a color-prior-guided two-channel grading method(CPG-Fusion)was proposed.The core of the method was a color prior attention module(CPAM):the per-image relative darkness,computed as a scene-brightness-independent physical cue,was fed as the attention input signal of a ResNet-18 backbone so that the CNN was consistently guided toward the region that directly corresponded to sooty mold coverage and was steered away from background spurious features.A dataset of 260 images with 4-level hazard index annotations was collected from two solar greenhouses in Changping,Beijing;the CNN was trained under a 5-fold stratified cross-validation protocol,and the deployed prediction was obtained by softmax probability averaging across folds.On 58 cross-scene OOD images independently collected in a second greenhouse(GH2),CPAM raised the quadratic weighted Kappa(QWK)from 0.401 of the ResNet-18 baseline to 0.551,improved the recall of the light-damage level by 22 percentage points,and confined all misclassifications to adjacent levels.In the same-protocol horizontal comparison with four representative attention modules(SE-Net,CBAM,ECA and Coordinate Attention),CPAM matched the upper bound of accuracy of the four counterparts on the OOD ensemble while attaining the lowest across-fold standard deviation among the four self-attention modules,indicating better single-model reliability under small-sample weak supervision.Grad-CAM visualization and the near-zero marginal gain of late fusion between CPAM and the pixel prior channel jointly confirmed that the pixel color prior was internalized into the CNN feature map.The proposed method showed that injecting interpretable physical cues into the attention pathway can improve the robustness of deep learning models under complex agricultural environments,and can be directly deployed on existing canopy surveillance cameras without additional hardware or target-scene parameter tuning.关键词
温室白粉虱/煤污识别/冠层图像/序数分级/颜色先验注意力模块Key words
greenhouse whitefly/sooty mold recognition/canopy image/ordinal grading/color prior attention module分类
农业科技引用本文复制引用
杨月,刘云玲,刘亚雄,宋坚利,范桢..基于冠层图像煤污识别的温室白粉虱危害分级方法[J].农业机械学报,2026,57(18):81-92,12.基金项目
国家重点研发计划项目(2023YFD2001200)、国家现代农业产业技术体系项目(CARS-28)和中国高校产学研创新基金项目(2024WA014) （2023YFD2001200）
