Green citrus detection using 'eigenfruit', color and circular Gabor texture features under natural outdoor conditions

Abstract

A machine vision algorithm was developed to detect and count immature green citrus fruits in natural canopies using color images. A total of 96 images were acquired in October 2010 from an experimental citrus grove in the University of Florida, Gainesville, Florida. Thirty-two of the total 96 images were selected randomly and used for training the algorithm, and 64 images were used for validation. Color, circular Gabor texture analysis and a novel 'eigenfruit' approach (inspired by the 'eigenface' face detection and recognition method) were used for green citrus detection. A shifting sub-window at three different scales was used to scan the entire image for finding the green fruits. Each sub-window was classified three times by eigenfruit approach using intensity component, eigenfruit approach using saturation component, and circular Gabor texture. Majority voting was performed to determine the results of the sub-window classifiers. Blob analysis was performed to merge multiple detections for the same fruit. For the validation set, 75.3% of the actual fruits were successfully detected using the proposed algorithm.