AI for Agriculture in India

The Ministry of Agriculture and Farmers Welfare signed a Statement of Intent (SoI) with IBM for a pilot study on using Artificial Intelligence (AI) and weather technology solutions in agriculture. Does India have the data infrastructure necessary for the adoption of AI in agriculture?

Background  

Agriculture plays a vital role in India’s economy. As of 2018, 58 per cent of rural households depended on agriculture as their means of livelihood.  

According to the Department of Industrial Policy and Promotion (DIPP), the Indian agricultural services and agricultural machinery sectors have cumulatively attracted Foreign Direct Investment (FDI) equity inflow of about $2.45 billion and the food processing sector has attracted around $7.81 billion during April 2000 to June 2017. 

 The most popular applications of AI in Indian agriculture appear to fall into three major categories:

a.    Crop and Soil Monitoring – Companies are leveraging sensors and various IoT-based technologies to monitor crop and soil health.

b.    Predictive Agricultural Analytics – Various AI and machine learning tools are being used to predict the optimal time to sow seeds, get alerts on risks from pest attacks, and more.

c.    Supply Chain Efficiencies–  Companies are using real-time data analytics on data-streams coming from multiple sources to build an efficient and smart supply chain. 

It is estimated that AI and connected farm services can impact 70 million Indian farmers by 2020, thereby adding an approximate US$ 9 billion to farmer incomes. 

Analysis 

The Government of India has signed agreements with IBM India to undertake a pilot study to utilise Artificial Intelligence (AI) and weather technology solutions in agriculture in one district each, in three states. The statement of intent (SoI) was signed in the presence of Agriculture Minister Narendra Singh Tomar.  

Speaking on the occasion, Tomar said: "Enabling use of next-generation technologies such as AI and advanced weather data for better insights to make faster and more informed agricultural decisions is a testament to our commitment". It has been the government's assurance to bring ‘digital technologies to help our farmers to increase their income and transform Indian agriculture’, he said.

The pilot study will be conducted for the Kharif crop season 2019 in three districts - Bhopal, Rajkot and Nanded - in Madhya Pradesh, Gujarat and Maharashtra, respectively, an official statement said. 

IBM’s Watson Decision Platform will give solutions through AI and weather technology at village level/ farm level - providing weather forecasts and soil moisture information, working on a pro bono basis. This will help farmers take decisions regarding water and crop management, leading to better production decisions and enhanced productivity.

Previously, Microsoft had developed an AI-Sowing App powered by Cortana Intelligence Suite including Machine Learning and Power BI, in collaboration with the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT). The app sends sowing advisories to participating farmers regarding the optimal date to sow. 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. Microsoft has also collaborated with United Phosphorous (UPL), India’s largest producer of agrochemicals, to  create  the Pest Risk Prediction API. Farmers can receive automated voice calls that inform whether their crops are at risk of a pest attack, based on weather conditions and crop stage. 

Assessment  

Our assessment is that while India is aiming to position itself as an emerging AI superpower, it has no significant repository of indigenous commercial data systems. While there is a significant amount of spatial data in agriculture, much is available once a year during the growing season. Thus, it can be years before a statistically significant temporal data set, about a given field or farm is collected. Often, the data collected in the fields need extensive pre-processing (cleaning up) before it can be reliably used as input to AI algorithms.

The lack of standards, perceived poor transparency around data use and ownership, and the difficulty of gathering and sharing data is likely to lead to a situation where AI algorithm developers are still starved for data. Agricultural sector needs a far greater focus from policymakers in addressing these challenges.

According to our observation, the Digital India initiative saw a doubling of its budget allocation to ₹307 billion last year. We feel that the government is likely to encourage the use of artificial intelligence-based applications as they are likely to create over 2.1 million  jobs for the agriculture sector over the next decade.

We believe that the world needs to produce 50% more food by 2050 and only 4% of additional land will come under cultivation to meet this demand. We feel that AI holds the promise of driving an agricultural revolution at a time when the world must produce more food, using fewer resources.

Comments