A Survey on Comprehensive Rice Grain Quality Analysis using Deep Learning

Grains, particularly rice, constitute the cornerstone of agricultural revenue in India, serving as a vital cereal crop for domestic and international markets. While farmers prioritize yield during cultivation, grain quality emerges as the critical factor during processing and trade. Traditionally, quality evaluation relies on manual inspection, a labor-intensive and error-prone process that struggles to address impurities such as stones, cracked seeds, and foreign matter. This paper proposes an automated solution leveraging the VGG19 deep learning model to classify and grade rice grain quality. By extracting features like size, shape, eccentricity, and area through image processing, the system achieves higher accuracy, reduces human effort, and enhances efficiency compared to conventional methods. Experimental results demonstrate the model’s potential to revolutionize quality assessment, offering a scalable and cost-effective alternative for the agricultural sector. Keywords—Rice grain quality, VGG19, deep learning, image processing, quality grading, automation