Analyzing Surface Defects in Apples Using Gabor Features

Abstract

This paper describes different approaches for detection and identification of diseases in apples using computer vision. Our proposed algorithms analyze surface appearance of apple for defects using image features, viz. color and texture. For segmentation of Region Of Interest (ROI), K-means clustering is performed over the image pixels based on their intensity values. For creation of feature vector, combinations of Gabor Wavelets with different feature descriptors were explored. Comparative study has been carried out between Haralick features, Local Binary Patterns, and kernel PCA, to observe their performance over Gabor features. Classification is achieved via Support Vector Machines and K-Nearest Neighbors. For the task of disease detection, accuracy recorded was greater than 96.9% for Gabor+LBP approach and in range of 89.8% to 96.25% for Gabor+Haralick approach. Gabor+kernel PCA recorded lowest accuracy of 90%. For disease identification, combination of Gabor+LBP outperformed other combinations, recording highest accuracy ranging from 85.93% to 95.31%.