{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:39:03Z","timestamp":1773887943743,"version":"3.50.1"},"reference-count":12,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,9]],"date-time":"2017-04-09T00:00:00Z","timestamp":1491696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel method for identifying three varieties (Taikong 9, Tainan 11, and Taikong 14) of foundation paddy seeds. Taikong 9, Tainan 11, and Taikong 14 paddy seeds are indistinguishable by inspectors during seed purity inspections. The proposed method uses image segmentation and a key point identification algorithm that can segment paddy seed images and extract seed features. A back propagation neural network was used to establish a classifier based on seven features that could classify the three paddy seed varieties. The classification accuracies of the resultant classifier for varieties Taikong 9, Tainan 11, and Taikong 14 were 92.68%, 97.35% and 96.57%, respectively. The experimental results indicated that the three paddy seeds can be differentiated efficiently using the developed system.<\/jats:p>","DOI":"10.3390\/s17040809","type":"journal-article","created":{"date-parts":[[2017,4,13]],"date-time":"2017-04-13T02:39:17Z","timestamp":1492051157000},"page":"809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Novel Method of Identifying Paddy Seed Varieties"],"prefix":"10.3390","volume":"17","author":[{"given":"Kuo-Yi","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mao-Chien","family":"Chien","sequence":"additional","affiliation":[{"name":"Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,9]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A new method for identification of Iranian rice kernel varieties using optimal morphological features and an ensemble classifier by image processing","volume":"1","author":"MousaviRad","year":"2012","journal-title":"Majlesi J. Multimedia Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compag.2012.09.007","article-title":"Automatic classification of non-touching cereal grains in digital images using limited morphological and color features","volume":"90","author":"Mebatsion","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.compag.2016.07.020","article-title":"Identifying rice grains using image analysis and sparse-representation-based classification","volume":"127","author":"Kuo","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"64006","DOI":"10.1088\/0957-0233\/22\/6\/064006","article-title":"Intelligent classification methods of grain kernels using computer vision analysis","volume":"22","author":"Lee","year":"2011","journal-title":"Meas. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s10845-014-0902-y","article-title":"Automated thermal fuse inspection using machine vision and artificial neural networks","volume":"27","author":"Sun","year":"2016","journal-title":"J. Intell. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.camwa.2011.11.041","article-title":"Detection and classification of areca nuts with machine vision","volume":"64","author":"Huang","year":"2012","journal-title":"Comput. Math. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7516","DOI":"10.3390\/s90907516","article-title":"Remote-sensing image classification based on an improved probabilistic neural network","volume":"9","author":"Zhang","year":"2009","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jfoodeng.2014.07.001","article-title":"Fruit classification using computer vision and feedforward neural network","volume":"143","author":"Zhang","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_9","unstructured":"Gonzalez, R.C., and Woods, R.E. (2002). Digital Image Processing, Prentice Hall. [3rd ed.]."},{"key":"ref_10","unstructured":"Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C. (2009). Finding the convex hull. Introduction to Algorithms, The MIT Press. [3rd ed.]."},{"key":"ref_11","unstructured":"Hagan, M.T., Demuth, H.B., Beale, M.H., and Jesus, O.D. (2014). Neural Network Design, Oklahoma State University. [2nd ed.]. eBook."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jfoodeng.2010.07.016","article-title":"ANN-based method for olive Ripening Index automatic prediction","volume":"101","author":"Rocco","year":"2010","journal-title":"J. Food Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:32:18Z","timestamp":1760207538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,9]]},"references-count":12,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["s17040809"],"URL":"https:\/\/doi.org\/10.3390\/s17040809","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,9]]}}}