Machine Learning is transforming global agriculture and being used to address key challenges around sustainability, food security, and climate change. Precision agriculture driven by machine learning has key economic, environmental, and social impact. For example, early detection and prediction of nutrient deficient areas enables farmers to treat only the affected areas, improving yields, minimizing cost spent on chemicals, and reducing the amount of chemicals introduced into the environment. We will explore challenges and opportunities around machine learning in agriculture from both sides: why is machine learning hard in this domain, how does addressing these challenge broader our understanding of machine learning generally, what challenges exist around adoption within the global agricultural community? This session will feature speakers from both academia and industry and discussion around all of these areas.