Nitin Nitin

The project aims to develop AI models predicting starch digestibility in starchy foods (potatoes, rice) and assess the impact of clean label processing techniques. Objectives include: (a) AI models using imaging and DSC for individual foods, (b) AI models for starch digestibility in food-lipid systems, and (c) AI models integrating processing and product data.

To address fresh produce waste and spoilage, we propose AI-based predictive solutions: (a) rapid detection and quantification of spoilage microbes in low-resource settings, (b) prediction of spoilage risks based on ripening, microbes, and transport, and (c) semi-supervised domain generalization for these aims, integrating Aims 1 and 2 for data-efficient AI models.

Biofortification tackles global micronutrient deficiencies through crop improvement without added inputs. To optimize breeding programs, an AI toolkit is being developed to predict and evaluate nutritional quality using machine learning. Open-source resources will benefit broader initiatives, and a hackathon will engage UC Davis students in advancing agricultural innovation.