Microsoft AI to Help Indian Farmers in Increasing Crop Yields

Microsoft AI For an Indian Farmer

In a few dozen villages in Telengana, Maharashtra, and Madhya Pradesh, farmers are receiving automated voice calls, which inform them about whether their cotton crops are at risk of a pest attack, based on weather conditions, and crop stage.

Microsoft has developed an AI-Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI. “The app sends sowing advisories to participating farmers on the optimal date to sow. Farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they need is a feature phone capable of receiving text messages,” said Microsoft in December 2017.

According to Artificial Intelligence in Telecommunication Market report published by Coherent Market Insights, Artificial intelligence (AI) is group of methodology that focus on formation of intelligent machines with the help of human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. These features can be effectively integrated in various technologies. The app model is going to uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming. The model currently being used to predict the prices of tur, is scalable, and time efficient and can be generalized to many other regions and crops. Microsoft has developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices. 

Government of Karnataka can receive price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP). To calculate the crop-sowing period, historic climate data spanning over 30 years – from 1986 to 2015 – for the Devanakonda area in Andhra Pradesh was analyzed using AI. To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated. This data was used to build predictability and guide farmers to pick the ideal sowing week. 

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