Current challenges in a growing population, climate change, water supply demands, labor shortage, environmental pollution, and emerging pests and diseases place stress on agriculture. We are developing AI-enabled technologies and techniques such as new decision support tools for production management, improvement of biodiversity and conservation, and precision application of agricultural inputs. We are using various Machine Learning techniques to develop improved precision nutrient management tools and predictive models of greenhouse gas emissions from agricultural practices. We use different observed field data on weather, soil, crop, and management practices to train and validate AI-based models.
