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Developing machine learning tools for soil moisture data – The Ohio State University News

An Ohio State faculty member has launched a three-year federally funded project that will develop nationally available, high-resolution cyberinformatics tools to help farmers and others analyze and respond to soil conditions.
The work will be led by Steven Quiring, professor of geography and a core faculty member in Ohio State’s Translational Data Analytics Institute. A $499,000 grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture will support the project.
Currently, various different products are available for data on soil moisture and evapotranspiration, the process by which water is transferred from the land to the atmosphere by evaporation from the soil and transpiration from plants. However, there is no consensus about the best datasets to use to support on-farm decision-making, and the data are not easy to access or use.
Quiring’s new project will develop user-friendly tools for agriculture, agribusiness, natural resource management and science that use machine learning to integrate disparate satellite, in situ and model-derived data; downscale them to a usable scale; and disseminate them in near-real time.
The goal is to provide timely data at a higher spatial resolution than currently available, making it easier and faster to utilize the data for decision-making.
In addition to uses such as precision agriculture and irrigation scheduling, these farm-scale data are important for modeling crop yield and insect and disease outbreaks; water and other resource management; and mitigating the impacts of extreme weather such as droughts. Users will be able to access, analyze and visualize the data through a web portal.
“Obviously soil moisture and evapotranspiration are crucial factors in agriculture and natural resource management, and they are constantly changing conditions,” Quiring said. “Having timely access to these data will help enable better decision-making and, ultimately, better outcomes.”