Paper Title
Fruit Quality Evaluation Using K-Means Clustering Approach

Manual identification of defected fruit is very time consuming. In previous years, several types of image analysis techniques are applied to analyse the fruit quality. This work presents a novel defect segmentation of fruits based on texture feature with K-means clustering algorithm. This approach thus provides a feasible robust solution for defect segmentation of fruits. The proposed method can process, analyze, classify and identify the fruits images, which are selected and sent in to the system based on color and texture feature of the fruit. The recognition system that has been developed is able to recognize all the test fruit images which are being selected by a user from the fruit selection menu which is based on GUI block in MATLAB on the system. Apple is taken as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. Keywords— Segmentation, K-means Clustering, GLCM.