Artificial Intelligence for the Study of Agri-food Systems

The increasing demand for sustainable agriculture and food systems calls for innovative solutions that can optimize production processes, ensure environmental protection, and address societal challenges. This thesis explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to study various agro-environmental systems. Key areas of investigation include crop breeding, soil sensitivity to temperature variations, and the study of ultra-processed foods. Through advanced AI models, the research aims to provide predictive insights and decision support tools that enhance agricultural productivity and sustainability. The findings underscore the potential impact of AI-driven models in transforming the agri-food sector, offering new perspectives for policy makers, researchers, and stakeholders in fostering a more resilient and efficient agri-food system.