Quality grading of soybean seeds using image analysis

Image processing and machine learning technique are modified to use the quality grading of soybean seeds. Due to quality grading is a very important process for the soybean industry and soybean farmers. There are still some critical problems that need to be overcome. Therefore, the key contributions of this paper are first, a method to eliminate shadow noise for segment soybean seeds of high quality. Second, a novel approach for color feature which robust for illumination changes to reduces problem of color difference. Third, an approach to discover a set of feature and to form classifier model to strengthen the discrimination power for soybean classification. This study used background subtraction to reduce shadow appearing in the captured image and proposed a method to extract color feature based on robustness for illumination changes which was H components in HSI model. We proposed classifier model using combination of the color histogram of H components in HSI model and GLCM statistics to represent the color and texture features to strengthen the discrimination power of soybean grading and to solve shape variance in each soybean seeds class. SVM classifiers are generated to identify normal seeds, purple seeds, green seeds, wrinkled seeds, and other seed types. We conducted experiments on a dataset composed of 1,320 soybean seeds and 6,600 seed images with varies in brightness levels. The experimental results achieved accuracies of 99.2%, 97.9%, 100%, 100%, 98.1%, and 100% for overall seeds, normal seeds, purple seeds, green seeds, wrinkled seeds, and other seeds, respectively