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Microsoft India recently showcased several projects that make use of the company's cloud-based artificial intelligence, cognitive services and Internet of Things (IoT) technologies that can change the way citizens, enterprises and governments engage in healthcare services, agricultural practices, education and everyday work.
Many of these applications are being tested out or used in the Indian states of Andhra Pradesh, Karnataka, Telengana, Punjab, Tamil Nadu and Haryana among others. For example, in a few dozen villages in Telengana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage.
Whereas in Karnataka, the government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP).
"Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications," Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), stated in a Microsoft blog post.
To make you aware, the non-profit ICRISAT conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world.
In partnership with ICRISAT, Microsoft has developed an AI-based Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI. The Sowing App was developed to help farmers achieve optimal harvests by advising on the best time to sow using data about weather conditions, soil quality and other indicators.
The best part; the 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 the company.
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.
MAI is the standardized measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops. This data is then downscaled to build predictability and guide farmers to pick the ideal sowing week.
This year, ICRISAT has scaled sowing insights to 4,000 farmers across Andhra Pradesh and Karnataka for the Kharif crop cycle (rainy season). Moreover, predictive analysis in agriculture is not limited to crop growing alone.
As mentioned earlier, Microsoft has also developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices.
The model feeds on remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming. The model is currently being used to predict the prices of tur.