Machine Vision Techniques for Improve Rice Grain Quality Analyzing Process

Rice grain quality inspection is a major process in rice production. To provide quality and accurate results in rice grain analyzing it is important to analyze rice grains one by one in a testing sample. In the current situation, most of rice grain producers inspect rice grains manually without using any automated process. The major problem is the accuracy of testing results depends on human quality because manually processes include human errors. The manual inspection of rice grains is a very complicated and time-consuming process due to these reasons most of the inspector's effect by external factors such as fatigue, tension etc. In this research, we provide a time-efficient and low-cost solution for reducing above-mentioned limitations by developing software. It uses modern image processing to analyze rice grains one by one efficiently over the manual examination. The quality of rice samples can be determined with the help of colour, and geometric features such as area, maximum length, maximum width and aspect ratio. This analyzing system designed and developed for measure area, maximum length, maximum width and aspect ratio by using Java programming language, morphological and colour operations in computer vision and finally the accuracy of the system tested by comparing manually tested sample and results from the system. According to the results, it shows this system provides more than 85 percent accuracy with confirming this was a better solution