From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild
作者机构：College of Information Science and TechnologyNanjing Forestry UniversityNanjing 210037China Institute of Forest Resource Information TechniquesChinese Academy of ForestryBeijing 100091China Key Laboratory of Forestry Remote Sensing and Information SystemNational Forestry and Grassland AdministrationBeijing 100091China Department of Computer Science and EngineeringUniversity of Nebraska-LincolnLincolnNE 68588USA School of EngineeringUniversity of WarwickCoventry CV47ALUK
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2023年第5卷第2期
页 面：195-208页
核心收录：
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
主 题：Plant breakthrough details
摘 要：Plant disease recognition is of vital importance to monitor plant development and predicting crop ***,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory *** 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 *** 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 ***,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty *** 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 *** effectively alleviates the problem of large interdomain *** adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass ***,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain *** 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.
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