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The trained MobileNet V2 model is used as a feature extractor, and (ii) the extracted features are input into the proposed multiclass TrAdaBoost algorithm for training and identification. Longjing tea images from three geographical origins, West Lake, Qiantang, and Yuezhou, are collected, and the tea from each geographical origin contains four grades. The Longjing tea from West Lake is regarded as the source domain, which contains more labeled samples. The Longjing tea from the other two geographical origins contains only limited labeled samples, which are regarded as the target domain. Comparative experimental results show that the method with the best performance is the MobileNet V2 feature extractor trained with a hybrid training dataset combined with multiclass TrAdaBoost with linear support vector machine (SVM). The overall Longjing tea quality identification accuracy is 93.6% and 91.5% on the two target domain datasets, respectively. The proposed method can achieve accurate quality identification of Longjing tea with limited samples. It can provide some heuristics for designing image-based tea quality identification systems.<\/jats:p>","DOI":"10.1007\/s40747-023-01024-4","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T10:02:47Z","timestamp":1680084167000},"page":"3409-3428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["An instance-based deep transfer learning method for quality identification of Longjing tea from multiple geographical origins"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0322-6059","authenticated-orcid":false,"given":"Cheng","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3106-021X","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Guodong","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Bincheng","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"1024_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app10031173","volume":"10","author":"Z Hong","year":"2020","unstructured":"Hong Z, He Y (2020) Rapid and nondestructive discrimination of geographical origins of longjing tea using hyperspectral imaging at two spectral ranges coupled with machine learning methods. 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