A gardener hoping for a crop of the juiciest summer tomatoes might tend to each and every plant in a plot. But a farmer working to feed the world?
Researchers believe that may be possible. They’re applying and integrating layers of technologies – including sensors, machine learning, artificial intelligence, high-throughput phenotyping platforms such as drones and small-scale rolling robots that can also fertilize, weed and cull single plants in a field – with the ultimate goal of replacing farmers’ reliance on heavy machinery and broadcast spraying in operations of all sizes.
Our. Dr. Fernando Miguez has been awarded a USDA National Institute of Food and Agriculture grant. These grants focus on big data analytics, machine learning, artificial intelligence, and predictive technologies needed to keep U.S. agriculture on the leading edge of food and agricultural production. These grants are awarded through the USDA-NIFA Agriculture and Food Research Initiative (AFRI) Food and Agriculture Cyberinformatics and Tools (FACT).
“Big data and artificial intelligence will increasingly play a vital role in the future of agricultural technologies,” said Parag Chitnis, acting director of USDA-NIFA. “As we work to realize precision nutrition for consumers and enhance farmer profitability and agricultural sustainability, these predictive technologies will keep research and development moving quickly to provide the tools needed for success.”
A new federally funded project led by Iowa State University researchers will help farmers share data relevant to their operations with one another and improve production.
The Smart Integrated Farm Network for Rural Agricultural Communities (SIRAC) project recently received a three-year, nearly $1.5 million grant from the National Science Foundation to develop technology that will allow farmers to pool data and share knowledge to guide responses to production obstacles such as weeds, disease and pests. The effort will start out as a small pilot project and gradually expand to hundreds of farmers. The multidisciplinary research team will pair innovative data gathering methods with machine learning to make the information easily accessible to farmers in the program, said Asheesh Singh, a professor of agronomy at Iowa State and principal investigator on the grant.
Using machine learning to develop and utilize plant breeding tools that can deliver improved genetics to farmers faster is a dream of Asheesh (Danny) Singh, associate professor of agronomy at Iowa State University and recipient of the 2020 Raymond and Mary Baker Agronomic Excellence Award.
Singh, the Monsanto Chair in Soybean Breeding at Iowa State, collaborates across disciplines with fellow innovators, combining artificial intelligence and genetics to speed selection of crop varieties finely tuned to the needs of farmers now and in the future.
The soil fertility tests farmers have used for decades to measure nitrogen levels don’t account for the vast majority of the nitrogen in soils, so Iowa State University scientists helped develop a new test that yields more accurate results by using soil biology.