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ruralconnectnews.com > Blog > India Region > Can technology make India’s crop insurance payouts more accurate?
India Region

Can technology make India’s crop insurance payouts more accurate?

Rural Connect News
Last updated: 03/06/2026 6:43 AM
Rural Connect News 4 hours ago
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For millions of Indian farmers, the Pradhan Mantri Fasal Bima Yojana (PMFBY) has long been a promise of security that often arrived too late – or not at all. Delayed claims, inaccurate loss assessments, and disputes over damage have plagued the scheme, leaving farmers waiting six months to two years for settlements .

Contents
From Crop Cutting Experiments to AI-powered precisionDetecting crop loss before damage is visibleAI assistants delivering personalised supportReal-time monitoring and risk assessmentWhat still needs workThe bottom line

Now, technology is rewriting that story. From satellites and AI-driven yield models to voice-based assistants, digital tools are making crop insurance payouts faster, fairer, and more transparent.

From Crop Cutting Experiments to AI-powered precision

Traditionally, insurance payouts relied on Crop Cutting Experiments (CCEs) – manual sampling of small subplots to estimate yields across entire regions. The process was slow, labour-intensive, and prone to human error. With hundreds of thousands of CCEs required each season, turnaround times stretched from months to years .

The government’s YESTECH (Yield Estimation System using Technology) initiative has changed this. Rolled out for paddy and wheat from Kharif 2023, and for soybean from Kharif 2024, YESTECH uses satellite imagery, Unmanned Aerial Vehicles (UAVs), and AI/ML models to estimate yields at the Gram Panchayat level . This eliminates the need for extensive manual sampling and reduces dispute-prone subjectivity.

The results have been dramatic. In a partnership with agtech firm Cropin, AI-powered predictive modelling reduced the number of required CCEs by 48 per cent, saving the government millions of dollars while accelerating claim settlements for over 2 million farmers across 25 districts .

Detecting crop loss before damage is visible

Early-season risks – such as prevented or failed sowing due to floods – have historically been difficult to assess quickly. A 2025 study in coastal Odisha demonstrated how radar Sentinel-1 satellite data can map prolonged flood conditions before the PMFBY cut-off date for claims .

The radar-based system correctly detected prevented or failed sowing conditions at 92.3% accuracy, identifying over 6,400 hectares of rice area eligible for claims in the 2018 wet season alone. Since radar penetrates cloud cover, it works even during monsoon when optical satellites are blind .

AI assistants delivering personalised support

Technology is also reaching farmers directly. KshemaGPT, an AI-powered assistant, provides personalised crop insurance support in local languages via voice or text. Farmers can check claim status, submit grievances, and receive crop-specific advice through a simple conversational interface .

The system integrates user data, policy records, and specialised crop models – such as Dhenu2-In-Llama3.1-8B-Instruct, trained by KissanAI – to answer complex questions about disease management and pest control tailored to specific regions .

Real-time monitoring and risk assessment

Insurance companies are also transforming their backend. The state-owned Agriculture Insurance Company (AIC) won the Geospatial Excellence Award in 2025 for integrating satellite intelligence, remote sensing, and digital field monitoring into its ecosystem .

“By enabling data-backed risk assessment, faster claim verification, and transparent settlement processes, AIC is evolving towards a more accurate and farmer-responsive protection framework,” said Lavanya R. Mundayur, CMD of AIC .

The government’s Krishi Decision Support System (Krishi-DSS) – a cloud-based geospatial platform – hosts satellite images, soil layers, weather data, and water-related information, supporting everything from drought monitoring to insurance claim verification .

What still needs work

Despite these advances, challenges remain. Poor rural internet connectivity limits access to mobile-based satellite services, and complex geospatial data is not easily understandable by the average farmer . Agricultural extension workers lack training in remote sensing tools, and many small farmers remain unaware of the digital advisories available to them.

For technology to truly transform crop insurance payouts, infrastructure and farmer education must catch up.

The bottom line

Yes, technology can – and is – making India’s crop insurance payouts more accurate. Satellite-based yield estimation, AI-driven sampling, real-time flood detection, and voice-based assistants are already delivering faster, fairer settlements to millions of farmers. With continued investment in rural connectivity and farmer training, the vision of instant, transparent, and accurate payouts is within reach.

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TAGGED: AI yield estimation, Crop Cutting Experiment, crop insurance, PMFBY, satellite technology, YESTECH
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