AI-POWERED RICE AUTHENTICATION: ENSURING SUPPLY CHAIN INTEGRITY THROUGH VARIETY CLASSIFICATION (International Journal of Food and Nutritional Sciences, 2022)
For more than half of the world's population, rice is a staple diet. Since there are many different types of rice, it is essential to maintain the authenticity and quality of rice along the supply chain to build consumer confidence and ensure food security. Although limited genetic testing or manual procedures have been used historically to identify rice varieties, new technology breakthroughs have made more precise and efficient authentication methods possible. Expert visual assessment or restricted genetic testing are common traditional means of classifying rice varieties. Although these techniques can yield reliable results, they are labor- and time-intensive and might not be appropriate for large-scale supply chains. Creating a system that can reliably categorize rice varieties based on their morphological and genetic properties is the main problem. This entails using technology to distinguish between several rice strains and varieties that may appear identical to the unaided eye. As a result, as the world's rice market grows and supply networks get more intricate, there is an increasing demand for reliable techniques for rice variety authentication. Sustaining the integrity of the supply chain, avoiding fraud, and preserving consumer confidence all depend on ensuring that the rice being distributed matches the kind that was advertised. The goal of the project, "Food Authentication: Rice Variety Classification for Supply Chain Integrity," is to improve the precision and efficiency of rice variety classification by applying cutting-edge computer vision techniques and machine learning algorithms. In order to create a system that can correctly and autonomously identify rice types, this research trains models on large datasets of genetic information and photographs of rice. Precise variety classification is made possible by the extraction of nuanced genetic and visual characteristics through the integration of machine learning. This development has a lot of potential to preserve the integrity of the rice supply chain and guarantee that consumers get the kind and variety of rice they want.