Measuring nutrient and anti-nutrient levels in staple crops is crucial to addressing the global malnutrition challenge of micronutrient deficiencies aka hidden hunger. Traditional methods like wet chemical analysis are costly and inefficient for consistent (anti-)nutrient measuring across time and space. We propose using UAV hyperspectral sensors to indirectly estimate nutrient and anti-nutrient content in staple crops like rice. This project aims to enhance sustainable agriculture and improve food security by providing timely data for better farming practices. It involves field data collection, laboratory analysis, and stakeholder engagement, with the ultimate goal of fostering climate-smart agricultural practices and better nutrition worldwide.