A Comprehensive Study of AI-Driven Multi-Modal Imaging Techniques for Legume Classification, Quality Assessment, and Shelf-Life Prediction

Legumes like chickpeas, pigeon pea, dry beans, and green gram are very useful in terms of nutritional value and sustainability across the globe. Yet the process involved in the classification and grading of legumes as well as the life expectancy of the same crops is mainly manual, thus inconsistent and inefficient. This study therefore introduces a methodology that uses Artificial Intelligence (AI) together with multi-modal sensing technologies for the classification and shelf-life assessment of legumes. The approach uses the latest developments in deep learning for feature extraction, multi-modal data fusion and intelligent prediction in order to get precise varietal classification, quality evaluation and shelf-life prediction. Furthermore, temporal analysis is used to model the dynamics of spoilage for various storage conditions. This system would ensure increased robustness, scalability, and usability, while promoting the use of evidence-based decision making in post-harvest handling and agriculture supply chains. It would promote improvements in quality assurance, reductions in post-harvest losses, and precision agriculture.