IBM’s Watson Decision Platform for Agriculture aims to combine predictive analytics, artificial intelligence, weather data, and Internet of Things sensors to give farmers insights on ploughing, planting, spraying, and harvesting. IBM hopes that this platform will improve the future of food and crop years as it launched a global version of its cognitive tools for agriculture.
In addition, the Watson agriculture suite will include models for crops such as corn, wheat, soy, barley and potato to name a few. Models will also be tailored for geography.
Big Blue estimates that the average farm generates 500,000 data points a day. IBM's bet is that aggregating field, machine learning, and environmental data can yield insights and best practices on crops for farmers. Watson Decision Platform for Agriculture utilizes IBM PAIRS Geoscope, which uses geospatial data sets from satellites, drones, flights, weather models, and IoT sensors.
Separately, IBM launched Weather Signals, an AI tool that aims to predict how consumers will react to weather in real time. Businesses can use Weather Signals to better plan production, logistics, inventory, and supply chain.
Weather Signals, which will integrate with data analysis tools such as Tableau, is built on historical regional sales data combined with The Weather Company historical data. IBM argued that weather analytics applies to multiple industries.
