New technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers in India to increase their income through higher crop yield and greater price control, Microsoft India said.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.<\/p>\n
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).<\/p>\n
\u201cSowing 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,\u201d Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), said in a Microsoft blog post.<\/p>\n
The non-profit ICRISAT conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world.<\/p>\n
In collaboration with ICRISAT, Microsoft has developed an AI-Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI.<\/p>\n
\u201cThe app sends sowing advisories to participating farmers on the optimal date to sow. The best part – the farmers don\u2019t 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,\u201d the company said.<\/p>\n
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 analysed using AI.<\/p>\n
To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated.<\/p>\n
MAI is the standardised measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops.<\/p>\n
This data is then downscaled to build predictability and guide farmers to pick the ideal sowing week.<\/p>\n
This year, ICRISAT has scaled sowing insights to 4,000 farmers across Andhra Pradesh and Karnataka for the Kharif crop cycle (rainy season).<\/p>\n
Predictive analysis in agriculture is not limited to crop growing alone.<\/p>\n
The Karnataka government will start using price forecasting for agricultural commodities, in addition to sowing advisories for farmers in the state.<\/p>\n
Commodity prices for items such as tur, of which Karnataka is the second largest producer, will be predicted three months in advance for major markets in the state, Microsoft said.<\/p>\n
Microsoft has developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices.<\/p>\n
The model uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming.<\/p>\n
The model currently being used to predict the prices of tur, is scalable, and time efficient and can be generalised to many other regions and crops.<\/p>\n
IANS<\/p>\n
Picture courtesy: Alamy<\/p>\n","protected":false},"excerpt":{"rendered":"
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