I-Corps: An integrated tool for crop ecosystem monitoring, analysis, and prediction

Award ID: 2306392

The broader impact/commercial potential of this I-Corps project is the development of a software system that uses satellite optical data, weather information, and algorithms to support the precise monitoring, reporting, and yield forecasting of crops and grasses. The proposed technology may help growers to manage their crops in response to climate variation and extreme events such as drought, heatwaves, and flooding. The proposed system may help sugarcane and corn industries to maximize their biofuel feedstock production. Agricultural retailers and insurance companies also may benefit from using this software tool to reduce losses and increase sales. This I-Corps project is based on the development of an integrated software tool for crop ecosystem monitoring, analysis, and prediction, which may be used to assess, diagnose, and predict crop condition, yield, water use, and soil health. As crop production is affected by many factors, crop growers and various crop-related business often face moderate- to high-risk of low crop yield or failure. The proposed technology combines satellite images, climate data, modeling, and machine learning algorithms as well as in situ data to provide data and information on crop types, crop growth conditions (e.g., greenness, leaf area index, and biomass), carbon and water fluxes, grain yield, and soil health over individual crop fields at high spatial resolution (0.025 acre). The proposed software tool has been evaluated across multiple scales from individual crop fields to county, state, and regional fields. The goal is to identify business partners and customers who are involved in the production, processing, trade, and consumption of crop products as well as crop ecosystem conservation and sustainability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman