FISSURE IDENTIFICATION METHODS IN RICE SEEDS AFTER ARTIFICIAL DRYING

ABSTRACT New, efficient, low-cost techniques for image processing and alternative machine learning for seed processing are of academic and industrial interest. This study aims to identify fissures in bark and peeled rice seeds using X-ray and RGB image processing techniques and machine learning. Samples of three batches of rice seeds were used: a batch of seeds not subjected to drying (peeled seed), and the other two comprised of dried seeds, one containing seeds with husk and another containing huskless seeds; each sample comprised 100 seeds. Images in X-ray and RGB formats were provided in the sequence processed in ImageJ software and introduced in the machine learning software, where they were pre-processed using the appropriate filters and then classified by the J48 and linear discriminant analysis (LDA) classifiers. X-ray images obtained using differentiated equipment allow the identification of cracks in rice seeds using image processing techniques and the LDA classifier. Capturing images using RGB is a viable alternative. Using filters, either individually or in combination, may constitute an adequate alternative for rice seed classification.摘要 针对种子处理领域的新型高效、低成本图像处理与替代机器学习技术，兼具学术与工业研究价值。本研究旨在借助X射线与RGB图像处理技术及机器学习方法，识别稻壳与去皮水稻种子的裂纹缺陷。本次实验采用三批次水稻种子样本：一批为未经过干燥处理的去皮种子，另外两批为干燥种子，其中一批带有稻壳，另一批为无壳种子；每批次样本包含100粒种子。研究所提供的X射线与RGB格式图像，均按照ImageJ软件的处理流程完成预处理，随后被导入机器学习软件；在该软件中，图像通过适配滤波器完成预处理操作，再分别由J48分类器与线性判别分析（Linear Discriminant Analysis, LDA）分类器进行分类。采用不同设备采集的X射线图像，可借助图像处理技术与LDA分类器实现水稻种子裂纹的精准识别。采用RGB方式采集图像同样具备可行性。单独或组合使用滤波器，可作为水稻种子分类的有效替代方案。