India is working to expand artificial intelligence-based digital tools that could help farmers make decisions about crops, weather, soil and markets.
The new effort follows discussions between the Government of India and the Gates Foundation on strengthening AI-powered agriculture tools and exploring whether Indian models can also be used in other parts of Asia and Africa.
Farmers make several decisions during a crop season. They need to decide when to sow, irrigate, apply fertilisers, manage pests and sell their produce. Weather changes can affect many of these decisions.
AI-based systems can process large amounts of information from weather records, satellite images, soil data and crop observations. The aim is to turn this information into advice that farmers can use. The discussions around the new collaboration include tools for weather forecasts, soil health, agricultural advisory services, crop productivity and market information.
One of the major uses of AI in agriculture is the analysis of satellite imagery. Satellite images can help identify agricultural land, monitor crop activity and track changes over time. Google DeepMind’s agriculture models developed for India are already being used beyond the country for applications linked to crop monitoring, farm credit and food-security information.
For farmers, such technology could eventually support more localised advisory services. A digital system may identify changes in crop conditions across a large area faster than traditional field visits alone. The final value will depend on whether the information reaches farmers in a form they can understand and act upon.
ICAR is also expanding precision agriculture
India’s agricultural research system is already working on precision agriculture. ICAR’s Network Programme on Precision Agriculture involves 16 research institutes working on sensors, remote sensing, drones, satellites, AI and information technology.
The programme includes crop and soil monitoring, post-harvest quality monitoring, precision vertical farming and technology for livestock and aquaculture. These technologies could help farmers use water, fertilisers and other inputs more carefully.
For example, sensors and remote monitoring may help identify whether a particular part of a field needs attention instead of treating the entire field in the same way.
The challenge is making technology useful
AI technology can process information quickly, but farmers need practical answers. A farmer does not need a complicated data dashboard during a crop emergency. They need to know what action to take.
This makes language, local conditions and timing important. An advisory for a rice farmer in Punjab may not work for a farmer growing millets in Karnataka or horticultural crops in the Northeast. Digital systems need local crop and weather information to become useful at the farm level.
Access is another issue. Small farmers may not have expensive devices or the skills needed to use complex digital platforms.
Technology can support farm decisions, but it cannot remove every uncertainty. A digital system may provide a weather forecast, but farmers also understand local soil conditions, water availability and market behaviour.
The strongest approach may combine scientific data with local agricultural knowledge. Krishi Vigyan Kendras, agricultural universities and extension workers can also play an important role. They can help farmers understand and use digital recommendations rather than simply receiving automated messages.
Better market information could also matter
AI tools are not limited to crop production. The discussions around the India-Gates Foundation collaboration also include access to market and pricing information. Market information can help farmers compare prices and understand demand before deciding where to sell. This does not guarantee better prices.
Transport costs, quality standards and access to buyers still affect what farmers earn. But better information can give farmers more options.
India has developed many agricultural technologies, but reaching small and marginal farmers remains a major task. A useful AI system must work beyond research centres and technology companies. It must reach farmers in their own languages and provide advice linked to their crops and locations.
The new push to strengthen AI tools for agriculture could expand the role of digital technology in Indian farming. The real test will be simple. Can a farmer use the information to make a better decision in the field?
If the answer is yes, AI could become another practical farm tool alongside seeds, machinery and agricultural advice.
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