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

Authors:
Akira Mizushima;Renfu Lu
Affiliations:
United States Department of Agriculture, Agricultural Research Service, 524 S. Shaw Lane, Room 207, Michigan State University, East Lansing, MI 48824, USA;United States Department of Agriculture, Agricultural Research Service, 524 S. Shaw Lane, Room 224, Michigan State University, East Lansing, MI 48824, USA
Venue:
Computers and Electronics in Agriculture
Year:
2013

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Abstract

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.