Journal of Scientific Research and Reports

The India’s grain sector is vital to its agricultural economy, ensuring food security and supporting rural livelihoods. Gujarat is a leading grain-producing state, challenges like post-harvest losses, inefficient storage, and inconsistent quality assessment persist. Traditional manual inspection methods are increasingly inadequate to meet modern demands for speed and precision. In response, AI-based grain analysers have emerged as an innovative solution, offering rapid, objective, and standardized assessments of grain quality through advanced sensors, computer vision, and machine learning. This study, conducted across 10 districts in Gujarat with 50 respondents, highlights moderate awareness 38 percent of AI-based grain analysers. Factors influencing adoption include the perceived need, ease of use, and accuracy. However, barriers such as high costs, lack of skilled labor, and technical complexity hinder widespread implementation. The research also reveals a predominance of male respondents, limited higher education, and a reliance on small-scale mill operations handling paddy, wheat, and chickpeas. For broader adoption, efforts must focus on increasing awareness, improving affordability, and enhancing training and after-sales support. By addressing these challenges, AI grain analysers can revolutionize quality management in Gujarat’s grain sector, boosting efficiency, transparency, and global competitiveness.