For farmers across India, growing food is often as much about risk as it is about rain. The realities of small landholdings, erratic monsoons, pest attacks, and fluctuating prices continue to create uncertainty that even the best intentions of public policies have not fully erased. One of the key promises made to Indian farmers in the last decade has been that of security—through crop insurance.
Yet, the traditional mechanisms have often failed to deliver this promise in full. Crop-cutting experiments have been slow. Payouts have been delayed. The credibility of schemes like the Pradhan Mantri Fasal Bima Yojana (PMFBY), while widely implemented, has come under question. Now, with the convergence of satellite imagery and artificial intelligence (AI), India is exploring a different way of looking at risk—quite literally, from above.
Where the Sky Meets the Soil
India has over 150 million farmers, and nearly 85% of them operate on small plots. This fragmentation makes manual crop monitoring expensive and often unreliable. Satellites operated by ISRO, such as Resourcesat and Cartosat, now provide regular, high-resolution images of agricultural regions. When paired with artificial intelligence tools, these images help estimate crop area, forecast yields, and identify issues such as drought, pest damage, or excess rainfall.
Some early results are encouraging. In Telangana, a pilot project involving 7,000 chilli farmers showed a 21% increase in yield, an 11% improvement in prices, and a 9% reduction in input use—all within a single season. Farmers in the program reportedly saw profits rise by around $800 per acre, in a country where the average annual income for a farming household is about $1,500.
Andhra Pradesh has also tested AI-assisted sowing advisories, with reported yield increases of up to 30%. In other cases, AI-based pest detection tools have supported timely interventions among 3,000 farmers, helping reduce both crop damage and input costs.
The Push Toward Smart Insurance
One of the more significant applications of AI in agriculture is in crop insurance. Today, many farmers still decide what to plant based on previous seasons’ trends or peer advice. This often leads to overproduction of certain crops, market gluts, and sharp price drops. These fluctuations not only hurt farm income but also disrupt the functioning of insurance models, which rely on yield benchmarks and historical averages.
AI-enabled macro planning tools aim to shift this approach. These models analyze long-term data—historic yields, market prices, soil quality, and climate forecasts—to recommend crops suited to each district or agro-climatic zone. By helping farmers align crop choices with environmental and market realities, the models also reduce risks that lead to insurance claims. This more tailored approach allows insurers to better align premiums with projected outcomes rather than outdated averages.
Volatile crop prices remain a concern. Tomato prices, for instance, surged more than 300% between June and July 2023 due to supply issues, while onion prices crashed 32% in December that year following early harvests and unseasonal rain. Such events underscore the importance of proactive planning and risk modelling. With predictive analytics, insurance products can be designed to accommodate local variations and preempt coverage gaps.
Pilot programs are underway across several districts, combining public data and private platforms. The government has also proposed a roadmap that consolidates information—on soil, weather, land ownership, and prices—into tools that deliver actionable insights through SMS, apps, and voice assistants.
Soil degradation is a major hurdle in Indian agriculture, impacting productivity and insurance reliability alike. Around 30% of India’s soil has degraded, affecting more than 97 million hectares as of 2021. Conventional soil testing has not kept pace, with only about 8,000 labs available for over 150 million farmers.
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Soil Health and Reliable Coverage
To bridge this gap, AI-supported soil diagnostics using spectroscopy and image analysis are emerging as an efficient alternative. These tools allow real-time assessment of pH, nutrient levels, moisture, and organic content—right in the field. This reduces guesswork and supports more accurate input use.
For insurers, verified soil health data strengthens risk profiling and claim verification. Models can distinguish between losses due to environmental factors versus those tied to poor land quality. This enables more precise payouts, curbs fraudulent claims, and improves overall efficiency. When a farmer grows a crop suited to their soil and local conditions, the likelihood of failure drops. With soil data in place, insurance plans can be better tailored to each plot, linking coverage to actual growing potential.
Pest Prediction and Early Action
India loses up to $36 billion annually to pest-related crop damage, with risks rising due to shifting climate patterns. In response, the government launched the National Pest Surveillance System (NPSS) in 2024—an AI-based tool where farmers can upload images of pest-affected crops and receive immediate feedback.
Timely diagnosis reduces the spread and severity of infestations. For insurers, early alerts lower the chance of widespread loss, improving predictability. AI-driven systems also offer new ways to assess real-time risk, moving away from blanket assessments toward more dynamic, crop-specific models.
Market Access and Financial Inclusion
Effective insurance is only one part of the agricultural value chain. Access to markets, fair pricing, and timely financing also shape a farmer’s ability to withstand risk. AI-based e-marketplaces are now playing a role in this space. These platforms use IoT devices to offer real-time price forecasts and assess crop quality with precision.
Several tools in the market now support quality assaying, helping farmers meet buyer requirements and negotiate better rates. These systems also provide transparency in grading—an area long plagued by subjectivity and exploitation by middlemen.
As a result, financial institutions are beginning to view smallholders as less risky. When lenders can see records of practices, yields, and market linkages, they are more likely to offer credit. This improves access to loans and allows bundling of insurance products with input financing or procurement contracts.
India’s Position Globally
Remote sensing for agriculture has been in use globally for years, particularly in the United States. However, India stands out for the scale at which it is attempting to bring AI into public-sector schemes. With more than 1,500 agri-tech startups, the country is gradually shaping an ecosystem that blends state-led initiatives with private innovation and research.
A 2025 report referenced in policy discussions outlines a three-step approach to scaling AI in agriculture: building infrastructure and governance, creating and validating tools, and ensuring last-mile delivery. While frameworks are still evolving, some elements are visible in India’s strategy.
States like Telangana and Karnataka have launched dedicated AI missions in agriculture. At the national level, efforts such as AgriStack and the IndiaAI Mission focus on digitizing land records, integrating datasets, and creating shared platforms for farmers, insurers, and agribusinesses. These efforts remain in development, but they reflect an interest in more connected and responsive systems.
Challenges That Remain
Despite notable progress, several challenges continue to limit widespread adoption. Less than a fifth of Indian farmers use digital technology, and even fewer have regular access to AI-based services. Reasons include low awareness, digital illiteracy, infrastructure gaps, and affordability concerns.
With average annual incomes below $1,500 and more than half of farming households in debt, the ability to invest in unfamiliar tools is limited. Many farmers view technology as an added cost unless its benefits are immediately visible.
There is also a regulatory gap. At present, India lacks institutional mechanisms to evaluate and certify AI tools before deployment. Without official validation, both users and insurers are cautious. Failures or inaccurate results could damage credibility and trust.
A Future Built on Data and Trust
For AI and satellite-based systems to truly strengthen India’s agricultural insurance landscape, implementation must go beyond technology. Training, transparency, and local adaptation will be key. Farmers need tools that provide more than data—they need context, support, and assurances.
Pilot efforts in Telangana and Andhra Pradesh show that AI, when used in ways that reflect local conditions, can improve yields, reduce losses, and expand access to insurance and credit. But digital access and literacy remain serious barriers, especially in regions with limited extension services.
India’s current strategy involves collaboration among state governments, startups, researchers, and farmer organizations. Programs like AgriStack, IndiaAI, and NPSS represent early efforts to build a digital backbone for agriculture. Whether these systems ultimately result in secure, resilient livelihoods will depend on how well they are communicated, governed, and adopted across diverse farming landscapes. As climate risks intensify and food systems evolve, the future of agricultural insurance in India may lie not just in what is seen on the ground, but in what can now be seen from the sky