Grainalyze: A Hybrid Approach for Consumer Level Assessment of Rice Quality Based on Grain Morphology

Abstract

Rice is a staple food in the Philippines, and its quality significantly affects consumer preference, market value, and grain grading standards across different regions and local agricultural markets today. However, conventional rice quality assessment commonly relies on manual inspection by trained personnel, making the process time-consuming, subjective, and often inaccessible to ordinary consumers. This study proposes Grainalyze, a mobile-based artificial intelligence-assisted system for rice grain quality assessment using instance segmentation and image-based analysis. Image-based rice quality assessment using computer vision and machine learning techniques has been widely explored in previous studies [1], [7]. A total of 3,885 source images of rice grains were prepared and expanded to