{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T03:20:38Z","timestamp":1764904838834,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T00:00:00Z","timestamp":1572393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["RTI2018-098156-B-C53"],"award-info":[{"award-number":["RTI2018-098156-B-C53"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit\/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.<\/jats:p>","DOI":"10.3390\/rs11212546","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T05:18:26Z","timestamp":1572499106000},"page":"2546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions"],"prefix":"10.3390","volume":"11","author":[{"given":"Razieh","family":"Pourdarbani","sequence":"first","affiliation":[{"name":"Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2439-5329","authenticated-orcid":false,"given":"Sajad","family":"Sabzi","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran"}]},{"given":"Mario","family":"Hern\u00e1ndez-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo, Guerrero 39087, Mexico"}]},{"given":"Jos\u00e9 Luis","family":"Hern\u00e1ndez-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Division of Research and Graduate Studies, TecNM\/Technological Institute of Chilpancingo, Chilpancingo, Guerrero 39070, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2521-4454","authenticated-orcid":false,"given":"Gin\u00e9s","family":"Garc\u00eda-Mateos","sequence":"additional","affiliation":[{"name":"Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain"}]},{"given":"Davood","family":"Kalantari","sequence":"additional","affiliation":[{"name":"Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari 48181 68984, Iran"}]},{"given":"Jos\u00e9 Miguel","family":"Molina-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Agromotic and Marine Engineering Research Group, Technical University of Cartagena, 30203 Cartagena, Murcia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.13031\/2013.3096","article-title":"A survey of computer vision methods for locating fruit on trees","volume":"43","author":"Ceres","year":"2000","journal-title":"Trans. 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