From monsoon to markets: How AI can transform India’s agricultural landscape
AI has the potential to transform agriculture by enabling decision-making based on real-time data. What are the major AI-driven agri-initiatives India has taken to address structural challenges, from erratic weather to fragmented markets? Why is it important to ensure that the benefits of innovation in AI reach farmers in distant villages?
AI offers solutions to structural challenges, from erratic weather, information asymmetry and fragmented markets. (File) — Abhinav Rai
The rapid advancement of Artificial Intelligence (AI) technology has triggered debates over its benefits and associated risks. In agriculture, AI offers immense potential to address structural challenges like erratic weather, information asymmetry, and fragmented markets that have long affected farm productivity.
As the global population is projected to reach 9.7 billion by 2050, meeting future food needs will require current global agri-production to grow by up to 70%. But to what extent does AI offer scalable solutions to agri-productivity-related challenges for India? Can these technologies help small landholding farmers, a characteristic feature of Indian agriculture, to improve productivity and resilience?
How AI can transform agriculture
The recent rapid development of AI and associated technologies has the potential to transform agriculture by enabling decision-making based on real-time data. It offers solutions to structural challenges through precision farming, weather forecasting, agri-productivity enhancement, climate-smart agriculture, and supply chain optimisation. In doing so, it makes agrifood systems more resilient and sustainable.
AI has a strong ability in recognising patterns and relationships across large and diverse datasets, which can expedite more informed and data-driven decision-making. In agriculture, this can help farmers make better decisions on suitable crop selection by integrating information on weather forecasts, soil conditions, and market demand.
The Food and Agriculture Organisation (FAO), a specialised agency of the United Nations (UN), is developing the world’s first domain-specific foundational AI model based on its rich data and global expertise. The model is designed to provide real-time policy guidance, agronomic advice, and climate change-related strategies. What are the recent policy initiatives India has taken to exploit the full potential of AI for the sector?
How AI is reshaping Indian agriculture, and who it may leave behind
Building India’s AI-enabled agriculture ecosystem
India has positioned AI as the central pillar of farm policy, research, and investment architecture. The Union Budget 2026–27 has proposed ‘Bharat-VISTAAR’ (Virtually Integrated System to Access Agricultural Resources), a multilingual AI tool that seeks to integrate AgriStack portals and the Indian Council of Agricultural Research (ICAR).
The platform is designed for specific Indian agricultural conditions and seeks to reduce farm risk by providing customised advisory support relating to soil types, climate zones and crop varieties. This is said to be deployable in low-connectivity rural areas through mobile phones and farm equipment.
Notably, ‘Bharat-VISTAAR’ builds on the existing digital infrastructure that India has been developing over the past few years. In 2024, the country launched the ‘Digital Agriculture Mission’ as an umbrella scheme for various digital agriculture-related initiatives. The Mission has two components: Agri Stack and Krishi Decision Support System.
Agri Stack envisages a farmer-centric Digital Public Infrastructure (DPI), including a farmers’ registry with a unique farmer ID, geo-referenced village maps integrated with land records and ownership details, and a Crop Sown Registry with digital seasonal crop sowing data.
The Krishi Decision Support System (DSS) will monitor crops, soil, weather, and water resource-related parameters, including flood/drought and groundwater, in real-time with the help of geospatial data. The mission also aims to prepare detailed soil profile maps at a scale of 1:10,000 covering about 142 million hectares of agricultural land.
There are a few other AI-driven agri-initiatives like the National Pest Surveillance System (2024) for early detection of pest infestations and crop diseases, and Kisan e-Mitra (2023), an AI-powered chatbot for queries around agri-related government schemes.
AI’s emerging role in agriculture
But how do such policy initiatives enable more informed and real-time decision-making and help address structural challenges, including weather forecast, precision farming and crop management, soil health, yield estimation and market optimisation?
Weather forecasting
Weather is one of the most consequential parameters in a crop’s life cycle. This is particularly true for regions in low- and middle-income countries, where accurate weather forecasts remain limited. In India, only 55% of the net sown area is covered by irrigation and the rest is rain-fed and therefore, dependent on rainfall and vulnerable to changing weather patterns.
Traditional weather forecasting models required sophisticated infrastructure and high technological costs. But the new-age AI-powered weather forecasting models, such as Pangu-Weather and GraphCast, have the potential to provide relatively faster, accurate, localised forecasts at much lower costs. Once trained, these models can run on laptops instead of supercomputers.
India has already begun exploring the use of such models. In the Kharif season of 2025, it conducted a pilot test of one such AI-based model for local monsoon-onset forecasts across 13 states. The forecast information was communicated to farmers through SMS, and 31–52% of farmers accordingly adjusted their planting decisions.
However, the accuracy of these models also depends upon calibrating them with location-specific contexts and ground-station datasets. Now, AI models are also being tested for the prediction of extreme weather events.
Precision farming and crop management
Precision farming is an approach that uses advanced technologies and data to manage crops and soils with greater accuracy to ensure optimal agricultural productivity in a sustainable way. Moving beyond traditional practices, AI-enabled surveillance systems, including drones, are being used for monitoring crop health, identifying diseases, pests, or nutrient deficiencies.
They can also help in applying pesticides or fertilisers in large farms, reducing manual labour-intensive tasks. Technologies such as satellite data, GPS, sensors, and drones generate high-resolution, real-time data that helps reduce waste, optimise resource use, minimise environmental impact and improve overall productivity.
Soil health
Conventional soil analysis methods required lab tests, such as measurement of nitrogen, phosphorus, potassium (NPK), and pH, or manual field-based classification methods like FAO soil texture charts and USDA soil taxonomy. These methods are time and labour-intensive and require expert knowledge.
But AI and machine-learning tools are making it possible to monitor several soil and crop health-related parameters at various levels with better accuracy in response to real-time environmental conditions. For instance, in soil-type classification, image processing techniques are used to identify soil characteristics based on its colour and texture. This information helps farmers in identifying suitable crop types and required fertilisers.
Yield estimation and market optimisation
AI is also being used for yield estimation. These estimates can help farmers in their crop management and planning market strategies. Remote sensing datasets such as high-resolution Sentinel satellite images are frequently used in the yield prediction applications. Sentinel-2, for example, provides imagery at spatial resolutions of approximately 10 meters and a revisit time of about five days, enabling regular monitoring of crop conditions.
India is implementing two such initiatives, one is the YES-TECH initiative (Yield Estimation System based on Technology) at the Gram Panchayat-level for yield estimation and claim settlement under Pradhan Mantri Fasal Bima Yojana (PMFBY). It is used across 12 states for paddy, wheat and soybean crops.
Another initiative, FASAL (Forecasting Agricultural output using Space, Agro-meteorology and Land-based observations), is being implemented across 20 states for pre-harvest production forecasts of 11 major crops – paddy, wheat, jute, cotton, sugarcane, soybean, tur, gram, mustard, lentil, and rabi sorghum.
In addition, AI-led data-driven decision-support systems also help in supply chain optimisation. By offering information on production, price, demand, logistics, etc., such systems can support better post-harvest management by farmers and ensure better prices for their farm produce. These large data-driven systems can also provide a platform for all the stakeholders, from farmers, suppliers, distributors, and retailers, to collaborate and share information with transparency.
Challenges and way forward
The Indian agricultural sector is currently facing multiple challenges, including increasing weather uncertainty, extreme climatic events, declining farm sizes, depleting groundwater, and an increasing population. While the introduction of AI-led agri-tech seems transformative, its adoption may face several hurdles, including the digital divide.
More than 86% of Indian farmers have very small landholdings. According to a survey by the National Bank for Agriculture and Rural Development (NABARD), average landholding size has decreased from 1.08 hectares in 2016-17 to 0.74 hectares in 2021-22, a reduction of about 31% in just five years. For farmers operating at such scales, the benefits of AI depend on a range of factors, including affordability, accessibility, and local relevance.
Historically, India’s traditional agriculture lacked actionable data such as localised weather forecasts, crop types, soil requirements, and others. But AI-led systems promise to reduce some of these gaps. However, given the large population dependent on agriculture (nearly 46 percent of the population) and the economic condition of the majority of the farmers, ensuring that the majority of these AI services remain available on affordable public platforms is important.
Agricultural AI, therefore, needs to be treated as a public good, with efforts invested towards ensuring that the benefits of innovation in AI reach the farmers residing in the distant villages. Agricultural AI has the potential to make Indian agriculture more productive, climate-resilient, and, if implemented rightly with large-scale adoption, it can help to realise the long-held dream of doubling farmers’ income.
Post read questions
1. Artificial Intelligence has the potential to make Indian agriculture more data-driven and climate-resilient. Discuss.
2. AI-enabled agriculture represents a shift from reactive farm management to predictive and preventive decision-making. Discuss with reference to weather forecasting, pest surveillance and crop management.
3. AI can potentially address several structural constraints in Indian agriculture, from weather uncertainty and soil degradation to fragmented markets. Analyse the opportunities and limitations.
4. India’s Digital Agriculture Mission seeks to build the digital foundations for data-driven agriculture. Examine how AgriStack and the Krishi Decision Support System can facilitate AI-led agricultural transformation.
5. How can India balance innovation in agricultural AI with concerns relating to affordability, digital exclusion, data governance and farmer autonomy?
(Abhinav Rai is a Doctoral candidate at the Department of Geography, Delhi School of Economics, University of Delhi.)
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