Infected fruit part detection using clustering

Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations. Manual identification of defected fruit is very time consuming. The proposed paper presents defect segmentation of fruits based on surface color features with unsupervised K-Means clustering and Fuzzy C-Means algorithms. As the first step, the digital color images of defective fruits are pre-processed using Gaussian low-pass filter (GLPF) smoothing operator to remove noise. The images are then segmented with the purpose of separating the defects from the edible regions using proposed clustering algorithms. We used color images of fruits for Defect Segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. We have taken three fruits as a case study and evaluated the proposed approach using defected fruits. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising.