IBM is expanding its Watson-powered decision platform for agriculture and is launching a new global predictive agriculture solution for farmers.
The offering taps into The Weather Company, an IBM business that gathers historical, current and forecast data to create weather prediction models. It pairs that information with sensor data from IoT devices in the field to develop crop models aimed at improving yield forecast accuracy, generate value, and increase both farm production and profitability. IBM says its solution can help farmers make informed decisions about planning, plowing, planting, spraying and harvesting crops.
“These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment,” said Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM.
IBM estimates the average farm today generates 500,000 data points per day, and expects that number to hit 4 million data points by 2036.
But most of that data is not used for analytics. Lauria added that this data can be repurposed for developing AI-driven insights that can be shared between growers and enterprises across the global agriculture ecosystem.
Watson Decision Platform for Agriculture offers the tools and solutions needed to “help growers make more informed decisions about their crops,” she said. The platform assesses data in an electronic field record to identify and communicate crop management patterns. It helps track crop yields and the environmental, weather and biologic conditions that go into a good or bad yield, including things like irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields, IBM said.
IBM has developed new crop models for corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato. The models are currently available in the US, Canada, Mexico, and Brazil, as well as new markets across Europe, Africa and Australia.