TY - JOUR 
A1 - Pourdarbani, Razieh
AU - Sabzi, Sajad
AU - Hernández Hernández, Mario
AU - Hernández Hernández, José Luis
AU - García Mateos, Ginés
AU - Kalantari, Davood
AU - Molina Martínez, José Miguel
T1 - Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions
Y1 - 2019
SN - 2072-4292
UR - http://hdl.handle.net/10317/9317
AB - 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.
KW - Edafología y Química Agrícola
KW - Producción Vegetal
KW - Tecnologías del Medio Ambiente
KW - Remote sensing in agriculture
KW - Artificial neural network hybridization
KW - Environmental conditions
KW - Majority voting
KW - Plum segmentation
KW - 5102.01 Agricultura
KW - 3308 Ingeniería y Tecnología del Medio Ambiente
LA - eng
PB - MDPI
ER -