B. LurstwutC. PornpanomchaiMahidol University2018-12-112019-03-142018-12-112019-03-142016-01-01International Journal of Applied Engineering Research. Vol.11, No.9 (2016), 6800-680709739769097345622-s2.0-84994101991https://repository.li.mahidol.ac.th/handle/20.500.14594/40619© Research India Publications. This paper presents a machine vision application designed for rice seed germination analysis by using image processing and computer vision technology. The application is called “Rice Seed Germination Analysis (RSGA)”. RSGA consists of five main processing modules which are image acquisition, image preprocessing, feature extraction, quality control analysis and quality results. The experiments is conducted on six variation Thai rice seed species of CP111, RD41, Chiang Phatthalung, Sang Yod Phattalung, Phitsanulok 2 and Chai Nat 1 in Bangkok and Chiangmai province of Thailand. RSGA extracts four main features which are color, size, shape, and texture. Then, RSGA applies Artificial Neural Network techniques in crop germination prediction. The precision rate is 93.06 percent, with the speed 8.31 seconds per image.Mahidol UniversityEngineeringArticleSCOPUS