International Journal of Electrical and Computer Engineering (IJECE) Vol. 9, No. 5, October 2019, pp. 3495~3503 ISSN: 2088-8708, DOI: 10.11591/ijece.v9i5.pp3495-3503  3495 Journal homepage: http://iaescore.com/journals/index.php/IJECE Quality grading of soybean seeds using image analysis Sutasinee Jitanan 1 , Pawat Chimlek 2 1 Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Thailand 2 Department of Computer Science and Information Technology, Faculty of Science and Technology, Pibulsongkram Rajabhat University, Thailand Article Info ABSTRACT Article history: Received Sep 5, 2018 Revised Apr 7, 2019 Accepted Apr 20, 2019 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. Keywords: Image classification Soybean classification Soybean seed Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Sutasinee Jitanan, Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok 65000 Thailand. Email: sutasineec@nu.ac.th 1. INTRODUCTION Soybeans are an important agricultural crop that is widely consumed because it is an exceptional source of nutrients, with a high protein and very high oil content [1]. Soybean quality affects the pricing and quality of grain for cropping and consumption. Soybean diseases greatly reduce the economic value of soybean products and result in economic losses for the soybean industry and farmers. Thus, the development of a rapid and reliable method of detecting the appearance quality of soybeans is of great significance to soybean farmers and the soybean industry [2]. Various soybean diseases affect the seeds appearance in terms of size, shape, and color. Disease affected soybeans can be purple seeds, green seeds, wrinkled seeds, and small/split seeds. For the most part, the productivity of soybeans depends on the quality of grains, and that is why quality grading is a very important process for the soybean industry and soybean farmers. A grading machine is used to classify soybeans according to their quality. This machine can separate only foreign material and seeds with non-standard sizes. However, the machine cannot classify low-grade