Elementor #14538
August 12, 2026
Moving crop insurance beyond manual crop-cutting experiments
When a monsoon fails or a hailstorm flattens a field, how quickly a farmer gets paid under crop insurance depends on how quickly – and how accurately – the crop loss can be estimated. For decades, that estimate came almost entirely from Crop Cutting Experiments: physically harvesting a small sample plot and extrapolating the yield. YES-TECH – Yield Estimation System based on Technology – is the government’s attempt to make that estimate faster and more consistent, using satellites instead of relying on manual sampling alone.
YES-TECH is a technology-driven yield estimation system used under the Pradhan Mantri Fasal Bima Yojana (PMFBY), India’s national crop insurance scheme. Rather than replacing manual Crop Cutting Experiments (CCEs) outright, YES-TECH blends satellite-derived yield estimates with CCE data to produce a single, more defensible yield figure at the Insurance Unit (IU) level – typically a village or village cluster.
The system was developed after extensive testing and piloting across roughly 100 districts, with a manual published to guide states through implementation, methodology, and integration with existing PMFBY workflows.
YES-TECH’s technical approach is described as semi-physical – it isn’t a pure black-box machine learning model, but one built around an interpretable Radiation Use Efficiency (RUE) framework that simulates daily biomass accumulation from satellite-observed canopy greenness and vigour. That design choice matters: an interpretable, physics-based model is easier for agronomists to cross-verify and easier for regulators to accept than an opaque model that just outputs a number.
The outputs are blended using a mandated weighting – 30% weight to the satellite-derived yield and the remainder anchored in CCE data – specifically to ensure the model doesn’t drift too far from ground-verified reality while still capturing the speed and consistency benefits of remote sensing.
WHY THE BLEND MATTERS MORE THAN THE SATELLITE MODEL ALONEA purely satellite-driven yield estimate would be faster to produce, but regulators and insurers need confidence that the number is defensible when a farmer disputes a claim. Anchoring the model in CCE data, even at a minority weighting, is what makes the output usable for actual claims processing rather than just a research estimate. |
| Manual Crop Cutting Experiments only | YES-TECH (blended approach) |
|---|---|
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Sample-based, small number of plots per Insurance Unit |
Satellite coverage across the full Insurance Unit, cross-validated with samples |
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Results available weeks to months after harvest |
Progressive monitoring through the season, faster post-harvest reconciliation |
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Manual data entry, prone to transcription error
|
Direct digital upload via the CCE-Agri App to the National Crop Insurance Portal |
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Limited transparency into how a figure was derived |
Semi-physical model designed for agronomist cross-verification |
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Disputes resolved slowly, with limited independent reference |
Satellite record provides an independent cross-check on ground data |
YES-TECH’s satellite inputs run at moderate resolution (5–30m), which is well suited to Insurance Unit-level aggregation but can’t detect variation within an individual small farm. Localised events – a hail corridor, a flooded pocket, a pest outbreak on part of one field – can get averaged away once results are aggregated to the IU level.
Field experience has been mixed in early rollout. A widely discussed case in Madhya Pradesh’s soybean-growing districts saw dissatisfaction among farmers over claim settlements after YES-TECH was adopted for selected crops, with experts calling for continued independent assessment of accuracy alongside the technology rollout.
YES-TECH’s value depends on clean linkage between satellite-derived crop maps, cadastral boundaries, and CCE data collected by state agriculture departments – the same data-integration challenge that shows up across most Digital Agriculture Mission components. Where that linkage is incomplete, reconciliation between satellite and ground estimates becomes harder, not easier.
Adopting YES-TECH isn’t simply a matter of switching on satellite monitoring for a state’s Insurance Units. States that get consistent, defensible results tend to invest in three things beyond the model itself:
Experts reviewing the shift have generally converged on a pragmatic view: manual CCEs are theoretically sound but cumbersome, prone to inconsistency, and slow, and technology-based estimation – while imperfect – tends to produce smaller inaccuracies than pure manual sampling once implemented well. The push isn’t to declare YES-TECH perfect, but to keep improving its accuracy while it delivers faster, more transparent claim settlement than the system it’s replacing. That framing – “better than what came before, and still improving” – is a more honest way to evaluate any new agricultural technology than expecting it to be flawless on day one.
The specific value YES-TECH adds to this sequence is mostly in steps one and three – continuous season-long monitoring that CCEs alone can’t provide, and a faster, more defensible route to the final blended figure once harvest data comes in.
YES-TECH doesn’t operate in isolation. Its accuracy improves directly when it can draw on verified crop-type data from the Digital Crop Survey, rather than having to infer crop type from satellite signatures alone. This is a recurring pattern across India’s digital agriculture programmes: each component’s individual value goes up substantially once it’s integrated with the others, rather than treated as a standalone system.
Not currently. YES-TECH is explicitly designed as a blended approach, with a mandated minority weighting for satellite-derived yield alongside CCE data, rather than a full replacement of manual sampling.
Coverage has expanded progressively since piloting began, focused on major crops where satellite classification is well established; specific crop and state coverage should be verified against current PMFBY documentation, as this continues to expand.
Accuracy varies by crop, region, and season. Independent studies and field experience suggest technology-based estimation is generally more consistent than manual sampling at scale, though specific disputes – such as those reported in parts of Madhya Pradesh – show accuracy is not yet uniform everywhere.
The WINDS portal centralises hyperlocal weather data from Automatic Weather Stations and rain gauges at the taluk and Gram Panchayat level. YES-TECH’s yield model draws on this weather data alongside satellite imagery, making WINDS a foundational input rather than a separate, unrelated system.
No. Crops with distinctive, easily classified canopy signatures at moderate satellite resolution – like wheat or rice – tend to model more reliably than crops with more variable planting patterns or shorter, less distinct growth stages. This is part of why rollout has proceeded crop by crop and state by state, rather than as a single nationwide switch.
Yield models are only as good as the data feeding themfieldWISE integrates satellite yield models like YES-TECH with verified crop registry and weather data – so the estimate a state relies on is built on the clearest possible picture of the field. → See how fieldWISE handles yield estimation |
Government of India, PIB – Technological Advancements in Crop Insurance (YES-TECH & WINDS Portal): https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=1941597
Related on this blog: What Is the Digital Crop Survey (DCS)? · Precision Agriculture Explained
