{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T17:10:20Z","timestamp":1769361020767,"version":"3.49.0"},"reference-count":12,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11","funder":[{"name":"2016 Key Scientific Research Project of Shaanxi Department of Education","award":["16JS031"],"award-info":[{"award-number":["16JS031"]}]},{"name":"Science and Engineering Talents Foundation of Weinan Normal University","award":["2015ZRRC02"],"award-info":[{"award-number":["2015ZRRC02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:p> Plant identification is now attracting considerable attention due to its important applications in agriculture automation and ecosystems. Recently, deep learning-based plant identification methods have drawn increasing interest and shown favorable performance. However, existing methods do not consider plant spatial structure and their similarities explicitly. In this paper, we propose a robust spatial-structure siamese network (3SN) for plant identification, which has the following advantages: (1) It models the spatial structure of a plant by recurrent neural networks exploiting their capability to capture long-range dependencies among sequential data, which enables it to capture even a slight difference between a specific plant and distractors. (2) The plant similarity modeling is achieved effectively by a siamese network with large numbers of image pairs. In this way, the plant classification task and siamese learning task are learned jointly in a unified framework, where both can enhance and complement each other. Extensive experimental results show that the proposed 3SN method outperforms the state-of-the-art methods consistently. <\/jats:p>","DOI":"10.1142\/s0218001418500350","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T06:10:33Z","timestamp":1525327833000},"page":"1850035","source":"Crossref","is-referenced-by-count":10,"title":["Spatial-Structure Siamese Network for Plant Identification"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4204-6536","authenticated-orcid":false,"given":"Zhi-Yong","family":"Gao","sequence":"first","affiliation":[{"name":"School of Chemistry and Environment, Weinan Normal University, Weinan, Shaanxi 714099, P. R. China"},{"name":"Key Laboratory for Ecology and Environment of River Wetlands in Shaanxi Province, Weinan, Shaanxi 714099, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng-Xing","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Chemistry and Environment, Weinan Normal University, Weinan, Shaanxi 714099, P. R. China"},{"name":"Key Laboratory for Ecology and Environment of River Wetlands in Shaanxi Province, Weinan, Shaanxi 714099, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji-Feng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Chemistry and Environment, Weinan Normal University, Weinan, Shaanxi 714099, P. R. China"},{"name":"Key Laboratory for Ecology and Environment of River Wetlands in Shaanxi Province, Weinan, Shaanxi 714099, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi-Li","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Chemistry and Environment, Weinan Normal University, Weinan, Shaanxi 714099, P. R. 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