Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions
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AuthorPourdarbani, Razieh; Sabzi, Sajad; Hernández Hernández, Mario; Hernández Hernández, José Luis; García Mateos, Ginés; [et al.]
Knowledge AreaEdafología y Química AgrícolaProducción VegetalTecnologías del Medio Ambiente
SponsorsThis research was funded by the Spanish MICINN, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53. This project has also been supported by the European Union (EU) under Erasmus+ project entitled "Fostering Internationalization in Agricultural Engineering in Iran and Russia" [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP.
Realizado en/conUniversidad Politécnica de Cartagena; Universidad de Murcia; University of Mohaghegh Ardabili; Universidad Autónoma de Guerrero
Bibliographic CitationPourdarbani R, Sabzi S, Hernández-Hernández M, Hernández-Hernández JL, García-Mateos G, Kalantari D, Molina-Martínez JM. Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions. Remote Sensing. 2019; 11(21):2546. https://doi.org/10.3390/rs11212546
KeywordsRemote sensing in agriculture
Artificial neural network hybridization
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 ...
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