Digital agriculture can double farm incomes

June 30, 2020

New Delhi: Digital Agriculture is no more a fantasy. It is a reality that has started to gain traction in Indian soils.

Digital technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers to increase their income through higher crop yield, better health inputs management and effective market price connect.

Currently a localized phenomenon, mainly in pilot projects, digital agriculture is sowing hopes and yielding positive results. In some villages in Telangana, 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. In collaboration with the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), Microsoft has developed an AI-Sowing App powered by 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.

Artificial Intelligence and big data are going to be a game changer in the agriculture sector, and the government is aiming to collate about 80 per cent of such data by end of 2020. The data will help in framing the right policy and converge some projects in order to achieve the targeted development of farmers and the overall sector.

United Phosphorous (UPL) in collaboration with Microsoft has created a Pest Risk Prediction App that enables farmers to get predictive insights on the possibility of pest infestation. This empowers them to plan in advance, reducing crop loss due to pests and thereby helping them to double the farm income.

Microsoft has also developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices. The model uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming. This data along with other inputs such as historical sowing area, production, yield, weather, among other datasets, are used in an elastic-net framework to predict the timing of arrival of grains in the market as well as their quantum, which would determine their pricing.

Shifting weather patterns such as increase in temperature, changes in precipitation levels, and ground water density, can affect farmers, especially those who are dependent on timely rains for their crops. Leveraging the cloud and AI to predict advisories for sowing, pest control and commodity pricing, is a major initiative towards creating increased income and providing stability for the agricultural community.

AI can be used in multiple domains of agriculture. Indian agriculture has been mostly traditional and the farmers have relied upon their perfectly honed agriculture wisdom in raising crops and protecting them. However, the challenges have broadened. The today and the future can no longer be dictated by individual farmers’ cognitive abilities. Unpredictable climates and global markets influence agriculture today much more than it was ever in the past. Traditional farming practices and subsistence level of farming have not been able to realise the full potential of the Indian fields. And failure of monsoon in the country has often resulted in failure of farming and suicides of farmers. But the use of cutting edge technologies like Artificial Intelligence may help Indian farmers to choose the right crop and minimise the risks and raise farm incomes to decent levels. Digital agriculture is going to be the next fastest adopted technology in agriculture.

—India News Stream