Customized Weather Forecasts, Image Recognition Highlight IBM’s Foray Into Agriculture

Many in business were puzzled when IBM announced plans to purchase The Weather Company in 2016. Then last summer IBM revealed its first joint product called Deep Thunder, which provides clients with short-term, hyperlocal, customized weather forecasts. (Department stores, for example, could try to gauge how such forecasts could impact consumers’ behavior.)

Gee, could ag retailers use this kind of information, especially to help track nutrient movement?

Former Trimble veteran Sid Siefken, now Global Agriculture Solutions Lead at IBM, works alongside The Weather Company and has been tasked with leveraging the company’s technologies in ag. The firm does not really have an “out-of-the-box” product per se to offer the industry. Instead, it partners with clients to create individual solutions.

That said, Siefken sees some ways retailers can connect with his new company, the most simple being downloading its weather app on their mobile device.

In addition, IBM/The Weather Company can provide a custom set of APIs that power a highly accurate forecast on a dealership’s web page. DTN offers a similar service, but Siefken notes his firm also owns The Weather Underground, which has more than 220,000 personal weather stations feeding its forecasting model, ensuring better local accuracy. In fact, he says, The Weather Company was shown to be the most accurate forecaster in a study by ForecastWatch, the nation’s top authority in meteorological accuracy validation.

To address data management challenges, IBM has introduced a new GIS data platform called Physical Analytics Integrated Data Repository and Services (PAIRS). “The message here is in the scale and capability of the work that IBM can provide — for instance, if you would like to see all of the information from the Corn Belt integrated, collected, and available for analysis,” Siefken says.

A third tool from IBM is Watson Image Recognition. This resource has been used in medicine to detect melanomas. For agronomists, it could possibly help identify the growth stages of a particular crop or disease or weed pressures to control from in-field imagery.

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