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Open AccessArticle

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

1
Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
2
Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo, Guerrero 39087, Mexico
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Division of Research and Graduate Studies, TecNM/Technological Institute of Chilpancingo, Chilpancingo, Guerrero 39070, Mexico
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Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain
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Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari 48181 68984, Iran
6
Agromotic and Marine Engineering Research Group, Technical University of Cartagena, 30203 Cartagena, Murcia, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2546; https://doi.org/10.3390/rs11212546
Received: 8 September 2019 / Revised: 27 October 2019 / Accepted: 29 October 2019 / Published: 30 October 2019
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
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. View Full-Text
Keywords: remote sensing in agriculture; artificial neural network hybridization; environmental conditions; majority voting; plum segmentation remote sensing in agriculture; artificial neural network hybridization; environmental conditions; majority voting; plum segmentation
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MDPI and ACS Style

Pourdarbani, R.; Sabzi, S.; Hernández-Hernández, M.; Hernández-Hernández, J.L.; García-Mateos, G.; Kalantari, D.; Molina-Martínez, J.M. Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions. Remote Sens. 2019, 11, 2546.

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