project 2

Evapotranspiration (ET) estimation is important for precision agriculture water management. Mapping the ET temporally and spatially can identify variations in the field so that it is useful for evaluating soil moisture, and assessing crop water status. ET estimation can also benefit the water resources management and weather forecast.

Limited water supplies, increasing pumping costs, variable rainfall patterns, and competition with municipalities are some of the challenges faced by agricultural communities across the state. Consequently, timely delivery and efficient use of irrigation water are critical to the sustainability and long-term stability of agricultural production in Texas. Presently, irrigation scheduling decisions based on reference evapotranspiration (ET) are limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, information about in-field variability of crop water demand is seldom considered in the decision-making process. As a result, the efficiency of irrigation is often low, which leads to wasteful use of water in areas that are already plagued by scarce water resources.

In this project, we propose the development of an Unmanned Aerial System (UAS) based crop monitoring system that calculates crop water use for irrigation scheduling and increased water use efficiency. To do this, our system will take the advantage of big data analytics and Artificial Intelligence (AI) on the UAS-derived phenotypic data and infield weather data to calculate actual crop evapotranspiration, biomass accumulation, and determine the timing and quantity of irrigation water needed.