More than half of India’s population is engaged in farming but the sector contributes just 16 per cent to the country’s GDP. For this, Niti Aayog has partnered with IBM to develop a crop yield prediction model and provide real-time advice to farmers using Artificial Intelligence (AI). It aims to help the farmer with warnings of pest control and crop disease outbreaks with help of data from remote sensing (provided by ISRO), soil health cards, Indian Meteorological Department’s weather prediction, and other data such as soil moisture, temperature, crop phenology, among others.
But IBM and Niti Aayog are the not only ones using AI in agriculture. Microsoft started piloting deployment of its AI technologies with 175 farmers in Andhra Pradesh two years ago in June 2016. A Microsoft case study details work in Bairavanikunta village. Farmers there received messages on sowing date, tips on how well the land should be prepared, advice on the quanity and timing of fertiliser to be used, among other things. Crop yields improved 30 per cent.
It wasn’t an easy task. The region’s climate data of 30 years, from 1986 to 2015, was analysed using AI. Then the moisture adequacy was calculated. The success led Microsoft and ICRISAT (Microsoft’s partner, which provided on ground data, in the programme) to take the pilot to 3,000 farmers in 2017. The results were the same: yield increased by 10 per cent to 30 per cent.
According to Niti Aayog, farmers get as low as 20 per cent of the price paid by an end-consumer for fruits and vegetables – largely because of ineffective price discovery, supply chain inefficiency, and local regulations. AI can help solve those problems by improving crop yield with real time advisories, reduce crop failure with advance information of weather events and detection of pest attacks, spread best practices around sowing and farming, and help with a better prediction of crop prices. “Why should a farmer know it is AI as long as it solves his problem,” said Anant Maheshwari, president of Microsoft India. For the farmer, the solution in this case came via a series of text messages.
To make predictions of impact, Roy said, AI algorithms need data. Data from e-NAM, Agricultural Census (with data on over 138 million operational holdings), AGMARKET, and over 110 million soil health samples will help in predictive modelling.