Dr. Fang, along with Dr. Jianzhong Su, UTA math professor and chairman of the Department of Mathematics, is currently working on a four-year grant for an Alliance for Smart Agriculture in the Internet of Things Era project. This project features both research and undergraduate education. It will encourage graduate and undergraduate UTA STEM students to expand their opportunities in the agriculture sector. A team of graduate and undergraduate students under Dr. Fang and Dr. Su’s advice formed a Smart Agriculture learning community and are being mentored in a research oriented learning process through experiential learning, internship and research opportunities. There are serval research directions under this grand and one of them is a machine-learning enhanced smart irrigation system. The goal of this research project io optimize irrigation system including scheduling, equipment, and other factors. In order to optimize the irrigation scheduling, to have a comprehensive understanding of the soil moisture content is the first step. However, most of the municipal landscaping sites rely on a sparse network of point measurement to estimate irrigation requirement. To evaluate the feasibility of spatially-variable irrigation management, an innovative system known as Precision Irrigation Soil Moisture Mapper (PrISMM) was developed in which an Unmanned Aerial Vehicle (UAV) equipped with multispectral sensors is used to estimate volumetric water content (VWC) using a thermal inertia approach. PrISMM consists of four central components, including (1) high-resolution thermal and optical remotely-sensed data, (2) site-specific soil analysis, (3) surface energy balance modeling and (4) machine learning module.