文献类型：期刊文献

中文题名：From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

作者：Xinlu Wu[1] Xijian Fan[1] Peng Luo[2,3] Sruti Das Choudhury[4] Tardi Tjahjadi[5] Chunhua Hu[1]

第一作者：Xinlu Wu

机构：[1]College of Information Science and Technology,Nanjing Forestry University,Nanjing 210037,China;[2]Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China;[3]Key Laboratory of Forestry Remote Sensing and Information System,National Forestry and Grassland Administration,Beijing 100091,China;[4]Department of Computer Science and Engineering,University of Nebraska-Lincoln,Lincoln,NE 68588,USA;[5]School of Engineering,University of Warwick,Coventry CV47AL,UK

年份：2023

卷号：5

期号：2

起止页码：195-208

中文期刊名：Plant Phenomics

外文期刊名：植物表型组学（英文）

收录：Scopus;CSCD:【CSCD2023_2024】；PubMed;

基金：supported in part by the National Natural Science Foundation of China under 61902187;in part by the Joint Fund of Science and Technology Department of Liaoning Province and State Key Laboratory of Robotics under grant 2020-KF-22-04;in part by the Program of Jiangsu Innovation and Entrepreneurship.

语种：英文

中文关键词：Plant;breakthrough;details;

分类号：S431.9

摘要：Plant disease recognition is of vital importance to monitor plant development and predicting crop production.However,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory vs.field environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains.In this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial training.Specifically,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty regularization.The multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source domain.This effectively alleviates the problem of large interdomain discrepancy.Subdomain adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass variation.Finally,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain transfer.MSUN was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.