AI Case Study
ICRISAT and Microsoft enable Indian farmers to achieve up to 30% higher yields at trial through machine learning advice on when to sow crops
Microsoft India collaborated with ICRISAT to develop an AI-enabled sowing app. The app sends text messages to farmers on the optimal date to sow. The pilot programme, which began in summer 2016 in the states of Andhra Pradesh and Karnataka, expanded to more than 3,000 farmers in 2017 and resulted in an increase in yield ranging from 10% to 30% across crops. Machine learning prediction is based on 30 years of crop sowing period and climate data.
Public And Social Sector
ICRISAT and Microsoft "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." In summer 2016, a pilot program was ran for 175 farmers in the state of Andhra Pradesh. The program sent farmers text messages on sowing advisories, such as the sowing date, land preparation, soil test based fertilizer application, and so on.
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.
The real-time MAI is calculated from the daily rainfall recorded and reported by the Andhra Pradesh State Development Planning Society. The future MAI is calculated from weather forecasting models for the area provided by USA-based aWhere Inc. This data is then downscaled to build predictability, and guide farmers to pick the ideal sowing week, which in the pilot program was estimated to start from June 24 that year.
Ten sowing advisories were initiated and disseminated until the harvesting was completed. The advisories contained essential information including the optimal sowing date, soil test based fertilizer application, farm yard manure application, seed treatment, optimum sowing depth, and more. In tandem with the app, a personalized village advisory dashboard provided important insights into soil health, recommended fertilizer, and seven-day weather forecasts.
In 2017, the program was expanded to touch more than 3,000 farmers across the states of Andhra Pradesh and Karnataka during the Kharif crop cycle (rainy season) for a host of crops including groundnut, ragi, maize, rice and cotton, among others."
Pilot programme resulted in increase in yield ranging from 10% to 30% across crops
"Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI."
"According to Dr. Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), '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.'"
"For centuries, farmers had been using age-old methods to predict the right sowing date. Mostly, they’d choose to sow in early June to take advantage of the monsoon season, which typically lasted from June to August. But the changing weather patterns in the past decade have led to unpredictable monsoons, causing poor crop yields."
"Crop sowing period data calculated from historic climate data spanning over 30 years, from 1986 to 2015 for the Devanakonda area in Andhra Pradesh, daily rainfall record."