{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T17:55:30Z","timestamp":1774288530791,"version":"3.50.1"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"vor","delay-in-days":266,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vingroup Innovation Foundation (VINIF) annual research grant program","award":["VINIF.2019.13"],"award-info":[{"award-number":["VINIF.2019.13"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>A new modification of multi\u2010CNN ensemble training is investigated by combining multiloss functions from state\u2010of\u2010the\u2010art deep CNN architectures for leaf image recognition. We first apply the U\u2010Net model to segment leaf images from the background to improve the performance of the recognition system. Then, we introduce a multimodel approach based on a combination of loss functions from the EfficientNet and MobileNet (called as multimodel CNN (MMCNN)) to generalize a multiloss function. The joint learning multiloss model designed for leaf recognition allows each network to perform its task and cooperate with the others simultaneously, where knowledge from various trained deep networks is shared. This cooperation\u2010proposed multimodel is forced to deal with more complicated problems rather than a simple classification. Therefore, the network can learn much rich information and improve its generalization capability. Furthermore, a multiloss trade\u2010off strategy between two deep learning models can reduce the effect of redundancy problems in ensemble classifiers. The performance of our approach is evaluated by our custom Vietnamese herbal leaf species dataset, and public datasets such as Flavia, Leafsnap, and Folio are used to build test cases. The results confirm that our approach enhances the leaf recognition performance and outperforms the current standard single networks while having less low computation cost.<\/jats:p>","DOI":"10.1155\/2021\/5032359","type":"journal-article","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T22:30:09Z","timestamp":1632522609000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Leaf Recognition Based on Joint Learning Multiloss of Multimodel Convolutional Neural Networks: A Testing for Vietnamese Herb"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0443-612X","authenticated-orcid":false,"given":"Trinh Tan","family":"Dat","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2307-6713","authenticated-orcid":false,"given":"Pham Cung","family":"Le Thien Vu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3426-1009","authenticated-orcid":false,"given":"Nguyen Nhat","family":"Truong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5860-4294","authenticated-orcid":false,"given":"Le Tran","family":"Anh Dang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6973-0499","authenticated-orcid":false,"given":"Vu Ngoc","family":"Thanh Sang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4847-4366","authenticated-orcid":false,"given":"Pham The","family":"Bao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,9,24]]},"reference":[{"key":"e_1_2_9_1_2","unstructured":"Poisoning from fake herbal medicine on the rise in Vietnam 2017 https:\/\/english.vov.vn\/society\/poisoning-from-fake-herbal-medicine-on-the-rise-invietnam-349026.vov."},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2013.00177"},{"key":"e_1_2_9_3_2","article-title":"Current and future status of herbal medicines","volume":"1","author":"Verma S.","year":"2019","journal-title":"Veterinary World"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/computers8040077"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2006.07.072"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07064-3_33"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.14569\/ijarai.2013.020309"},{"key":"e_1_2_9_8_2","first-page":"166","article-title":"Automatic recognition of medicinal plants using machine learning techniques","volume":"8","author":"Adams B.","year":"2017","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2018.2879833"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.01.005"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"JiaD. 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