Automated Rice Grain Quality Assessment Using Computer Vision And Machine Learning Based On Physical Feature Extraction

Rice quality assessment plays a critical role in the agricultural and food industries, as it directly influences market value, consumer satisfaction, and food safety. Traditionally, rice grain quality evaluation has been performed manually by experienced inspectors based on visual observation and physical measurements. However, manual inspection is often time-consuming, subjective, and prone to human error. To overcome these limitations, this study proposes an automated rice quality analysis system using image processing and machine learning techniques. The proposed approach extracts important physical attributes of rice grains, including area, perimeter, width, height, aspect ratio, and major and minor axes, from digital images captured under controlled conditions. Image processing techniques such as grayscale conversion, binary thresholding, morphological operations, edge detection, and object detection are applied to accurately isolate and measure individual rice grains. The extracted features are then stored and used to train a machine learning model for classification. A Support Vector Machine (SVM) classifier is employed to categorize rice grains into different quality grades based on their physical characteristics. The performance of the proposed system is evaluated using a dataset consisting of multiple rice varieties. Experimental results demonstrate that the automated system achieves improved classification accuracy compared to traditional manual inspection methods. The proposed framework provides an efficient, reliable, and cost-effective solution for automated rice quality assessment. By integrating computer vision and machine learning techniques, the system reduces human dependency, improves consistency in quality grading, and has the potential to support large-scale deployment in agricultural industries and food processing units.