An image segmentation method for apple sorting and grading using support vector machine and Otsu’s method

Segmentation is the first step in image analysis to subdivide an image into meaningful regions. It directly affects the subsequent image analysis outcomes. This paper reports on the development of an automatic adjustable algorithm for segmentation of color images, using linear support vector machine (SVM) and Otsu’s thresholding method, for apple sorting and grading. The method automatically adjusts the classification hyperplane calculated by using linear SVM and requires minimum training and time. It also avoids the problems caused by variations in the lighting condition and/or the color of the fruit. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 300 ‘Delicious’ apples using three training samples with different color characteristics (i.e., orange, stripe, and dark red) and their combination. The segmentation error varied from 3% to 25% for the fixed SVM, while the adjustable SVM achieved consistent and accurate results for each training set, with the segmentation error of less than 2%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in a multi-channel color space, and it can be easily adapted for other imaging-based agricultural applications.

► A robust image segmentation method for apple sorting and grading was proposed.
► A contrast enhanced image was calculated by support vector machine.
► Optimal threshold for the contrast enhanced image was estimated by Otsu’s method.
► The proposed method achieved robust results with less than 2% segmentation error.
► Fast processing speed of 1.5 ms was achieved with 3.4 GHz CPU.