河南农业大学学报2026，Vol.60Issue(4)：705-714,10.DOI:10.16445/j.cnki.1000-2340.20260422.001
基于证据深度学习的小麦病虫害识别模型构建
Construction of wheat disease and pest identification model based on evidential deep learning
摘要
Abstract
[Objective]This study aims to develop a credible wheat disease and pest identification model to provide technical support for intelligent pest and disease management.[Method]In the inte-lligent recognition of wheat diseases and pests,EfficientNet is selected as an effective feature extrac-tor.A Dirichlet-based framework is utilized to derive uncertainty scores corresponding to model predic-tions,thereby extending the conventional single-class output of EfficientNet to a two-dimensional output comprising both classification results and uncertainty scores.This approach enhances the model's ability to express the trustworthiness of its predictions.Furthermore,the Jaccard index is employed to optimize the consistency between uncertainty score partitioning and classification correct-ness,enabling adaptive uncertainty threshold selection and further improving model credibility and recognition accuracy.[Result]Compared with the traditional EfficientNet,the introduction of the uncertainty mechanism increased the model's precision rate,recall rate,and F1 score by 5.31%,4.42%,and 4.81%,respectively;adding the threshold filtering strategy further improved them by 6.21%,4.33%,and 4.71%;the proposed method achieved performance improvements by 11.84%,8.94%,and 9.74%in the three indicators,respectively.The average uncertainty score for correctly predicted samples is 0.284,which is notably lower than 0.421 for incorrectly predicted ones.With an uncertainty threshold of 0.445 optimized via the Jaccard index,9.51%of high-uncertainty predictions are filtered out,significantly improving the model's credibility in complex environments.[Conclusion]The incorporated uncertainty mechanism allows the model to detect potential misclassifications in com-plex environments and trigger human intervention,thereby significantly enhancing the credibility of decision-making in agricultural applications.关键词
小麦/病虫害/高效网络/狄利克雷分布/雅卡德指数Key words
wheat/disease and pests/EfficientNet/Dirichlet distribution/Jaccard index分类
农业科技引用本文复制引用
虎晓红,车银超,陈宝钢,陈桂东,虎金峰,李勇..基于证据深度学习的小麦病虫害识别模型构建[J].河南农业大学学报,2026,60(4):705-714,10.基金项目
河南科技攻关项目(252102110340) （252102110340）
河南省自然科学基金面上项目(242300420285) （242300420285）
河南重点研发专项(231111211300) （231111211300）
