As learning outcomes, the students should be able to apply AI and machine learning to address natural resource problems, with attention to uncertainty and responsible use; build and evaluate models using agricultural data streams (field sensors, imagery, genomics/phenomics and economic data); translate model outputs into actionable decisions to improve profitability, sustainability, and resilience; communicate results to technical and non-technical stakeholders and evaluate adoption, risks, and policy considerations. The candidate will focus on biological questions of economic importance in horticultural crops and integrate artificial intelligence with data science, sensing technologies, forecasting, and decision frameworks to increase profitability and reduce risks by enhancing water efficiency and sustainable production, disease protection, precision nutrition, and food quality in high-value horticultural crops.