哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(4)：430-438,9.
基于证据深度学习的苹果叶片病害分类网络
Apple leaf disease classification network based on evidential deep learning
摘要
Abstract
This paper addressed the issue that current apple leaf disease classification networks focused primarily on improving classification performance,while neglecting the reliability of predictions and the estimation of cognitive uncertainty.This limitation led to unreliable results and overconfidence when handling abnormal scenarios and compound disease samples.To tackle this problem,introduced evidential deep learning to explicitly model cognitive uncertainty and constructed a new classification network named EUCNet,using a Swin-Transformer backbone.Furthermore,to enhance the reliability of predictions,an evidence-based decision mechanism was proposed that leveraged uncertainty estimation to identify and flag unreliable samples for manual review.Experimental results demonstrated that EUCNet maintained classification performance comparable to mainstream networks under normal conditions while exhibiting significant advantages in various simulated abnormal scenarios.Specifically,its evidence-based decision mechanism effectively distinguished between high-confidence samples and those requiring rejection,with EUCNet outperforming mainstream classification networks on both types of samples.Furthermore,when confronted with compound disease samples,EUCNet achieved a successful detection rate of 86.57%,further validating the reliability and practical utility of the proposed method.关键词
证据深度学习/苹果叶片病害分类/不确定性估计/复合病害识别/认知不确定性Key words
evidential deep learning/apple leaf disease classification/uncertainty estimation/compound disease identification/epistemic uncertainty分类
信息技术与安全科学引用本文复制引用
黄刘诚,张陈英帅,王捷,马晓剑..基于证据深度学习的苹果叶片病害分类网络[J].哈尔滨商业大学学报（自然科学版）,2026,42(4):430-438,9.基金项目
中央高校基本科研业务费资助项目(2572023DJ04) （2572023DJ04）
