Each topic below explains the evidence, the business problem it creates, how GLOBEIR solves it, the benefits to your organisation, and how your data stays private and secure.
1. Crop Mapping and Acreage Estimation
Knowing which crop is sown on which plot is the starting point for almost every agricultural decision, from input stocking to procurement and insurance. India does this at national scale today. Under FASAL, the Mahalanobis National Crop Forecast Centre (MNCFC) uses multispectral and microwave satellite data for crop mapping and combines satellite indices with weather data in yield models, with State Agriculture Departments collecting the ground data used to train the crop maps [5]. Government uses these satellite-based estimates for decisions on storage, pricing and import or export [6].
The ground layer is now being digitised as well. AgriStack's Crop Sown Registry is fed by a seasonal Digital Crop Survey, which in Kharif 2025 covered more than 28.5 crore plots in 604 districts [13] and in Rabi 2025-26 covered more than 31.3 crore plots in 648 districts [14]. The Ministry notes that the survey has given plot-level visibility of crops and better estimation of sowing patterns, supporting evidence-based planning for procurement, input supply and logistics [13]. Chhattisgarh, for example, has used Farmer ID and the Digital Crop Survey for MSP paddy procurement covering over 32 lakh farmers in a single season [13].
Satellite classification and plot surveys are complementary. The survey records what a surveyor saw on a plot on a given date; satellite time series show the whole landscape, every few days, and can flag plots where the recorded crop and the observed growth pattern do not match. Together they give area figures that are both complete and checkable.
Business problem
Agribusinesses, lenders and procurement agencies still plan largely on district or state acreage that arrives late and hides local variation. Government survey data is held by states and is not always available to private users at plot level, while satellite-only maps on very small fields need careful validation. Teams therefore struggle to answer a simple question in time to act: how much of which crop is in the ground in each block this season?
Our solution
- Crop type maps from multi-date Sentinel-1 radar and Sentinel-2 optical imagery, built with machine learning and validated against geotagged field points [11][12].
- Ground truth collection in the My GLOBEIR app, with forms that can mirror Digital Crop Survey fields so that client data and public registries line up where access is permitted.
- Acreage tables by village, block and district, with accuracy reported alongside each figure, delivered on WebGIS dashboards.
- Mismatch flags where recorded crops, client records and satellite growth patterns disagree, so field teams know where to check.
Benefits
- Within-season acreage at the level where stock, credit and procurement decisions are made.
- Industry example: FASAL already produces satellite-based production estimates for 11 major crops in 557 districts [5].
- Industry example: Digital Crop Survey data now supports procurement, input supply and logistics planning [13].
- Validated, repeatable maps that build a multi-season record of cropping patterns.
Privacy & data security
- Plot-level crop records linked to a Farmer ID or land record are personal data under the DPDP Act [21].
- Acreage analysis runs on plot geometry and crop class; farmer identifiers are not needed and are removed or pseudonymised before analysis.
- AgriStack data is used only where the client has lawful, consented access under state arrangements [14].
- Outputs for sales and planning teams are aggregated to village or block level.
2. Crop Health and Stress Monitoring
Healthy green vegetation reflects strongly in near-infrared light and absorbs red light. The Normalized Difference Vegetation Index (NDVI) captures that contrast in a single number per pixel, so a time series of NDVI shows how a crop is establishing, peaking and maturing. Comparing the current season with the normal curve for the same crop stage reveals where growth is lagging. Sentinel-2 adds red-edge bands that are sensitive to chlorophyll, at a designed revisit of five days [11].
India uses these indices operationally. MNCFC monitors remote sensing drought indicators, including soil moisture, NDVI and the Land Surface Wetness Index, follows the methodology of the New Manual for Drought Management 2016, provides technical support to states and vets every drought memorandum the Department receives [20]. Government has also deployed an AI-based National Pest Surveillance System used by over 10,000 extension workers, covering 65 crops and over 400 pests from photographs taken in the field [13].
Satellite indices tell you where a problem is; they rarely tell you what it is. A dip in NDVI may come from moisture stress, nutrient deficiency, pest attack, waterlogging or simply late sowing. Good stress monitoring therefore pairs the satellite signal with targeted field checks and, where relevant, weather and soil layers.
Business problem
Agronomy teams, contract-farming companies and lenders typically learn about crop stress from complaints or field visits, after yield is already lost. With thousands of small plots per district, field staff cannot see everything, and visits are spread evenly rather than where the crop is struggling. Monsoon cloud also interrupts optical imagery exactly when Kharif crops are most at risk.
Our solution
- NDVI and red-edge time series by plot, village or block, with anomaly maps against a multi-year baseline for the same crop stage, using our remote sensing workflows.
- Radar backscatter from Sentinel-1 to keep monitoring through cloudy weeks.
- Stress alerts pushed to a monitoring dashboard and to field teams, with a Heatmap view of where alerts cluster.
- Diagnosis visits logged in the My GLOBEIR app with photos, so each alert is closed with a recorded cause.
Benefits
- Field visits directed to stressed areas rather than random samples.
- Industry example: India's national drought assessment already relies on NDVI, wetness and soil moisture indicators [20].
- Industry example: AI-based pest identification from field photos is in use across 65 crops [13].
- A season-long record of crop condition that supports advisory, credit monitoring and claims.
Privacy & data security
- Vegetation indices describe land, not people; they become personal data only when linked to a farmer's plot record [21].
- Field photos are collected for the agreed purpose of crop condition assessment only and are covered by the same access controls as other records.
- Alerts shared with partners show the plot or area, not the farmer's identity, unless the agreed purpose requires it.
- All imagery products and field records are encrypted in transit and at rest, with role-based access.
3. Soil Health Mapping and Variable-Rate Nutrients
The Soil Health Card scheme analyses soil samples for 12 parameters, including pH, electrical conductivity, organic carbon, nitrogen, phosphorus, potassium, sulphur and micronutrients such as zinc and boron, and gives farmers nutrient recommendations [8]. More than 25.17 crore cards had been issued by July 2025, supported by 1,068 static, 163 mobile, 6,376 mini and 665 village-level soil testing laboratories [7][8]. Advisory reaches farmers through ATMA, Krishi Vigyan Kendras and trained Krishi Sakhis [7].
Each card is a point measurement. Interpolating many geotagged sample points creates continuous nutrient surfaces, and combining those surfaces with multi-season crop vigour maps produces management zones: parts of a field or village that respond differently to inputs. ICAR institutes have developed precision tools that depend on such zoning, including an image-based variable-rate nitrogen applicator [8]. At national level, the Digital Agriculture Mission plans soil profile maps at 1:10,000 scale [2].
Business problem
Fertiliser companies, FPOs and agri-service providers have soil test data, but it usually sits on paper cards or in tables without coordinates. Recommendations stay at a flat dose per crop, which overspends on low-response patches and under-feeds productive ones. Companies promoting speciality nutrients cannot see where deficiencies cluster, so promotion and demonstrations are poorly targeted.
Our solution
- Geocoding and quality checks of soil test records, and interpolation into nutrient and pH surfaces with uncertainty maps.
- Management zones from soil surfaces and multi-season vigour, drawn for agronomist review on WebGIS.
- Deficiency maps by block and village to target micronutrient and soil amendment programmes.
- Zone files exported in standard formats for variable-rate equipment or drone spraying plans.
Benefits
- Input recommendations based on measured soil variation rather than a single flat rate.
- Industry example: Soil Health Cards already test 12 parameters, giving a rich base for nutrient mapping [8].
- Industry example: ICAR has developed image-based variable-rate nitrogen application [8].
- Better targeting of demonstrations and speciality nutrient promotion.
Privacy & data security
- Soil Health Card records often carry farmer names and survey numbers; these identifiers are separated from the soil values before mapping [21].
- Published nutrient surfaces are continuous maps that do not reveal individual cards.
- Client-owned soil data is used only for the agreed programme and never shared with other clients.
- Access to sample-level data is restricted by role, with audit logs.
4. Irrigation Planning and Water Use
More than 80 per cent of India's available water resources are used for irrigation, yet only about half of the net sown area has irrigation facilities [16]. The Per Drop More Crop scheme has brought over 115 lakh hectares under micro-irrigation as of July 2026, about 8.11 per cent of the net sown area, offering 55 per cent assistance to small and marginal farmers and 45 per cent to others, and it prioritises water-scarce and groundwater-stressed regions [16]. In a documented case from Palnadu, Andhra Pradesh, drip irrigation with fertigation on an acid lime orchard raised yield from 90 to 105 quintals per acre while cultivation cost fell from Rs 3.0 lakh to Rs 2.4 lakh [16].
Remote sensing supports water decisions at three levels: mapping which areas are irrigated and from what sources; estimating crop water stress from thermal, vegetation and wetness indices; and overlaying groundwater status from the national assessment [9]. ICAR has developed water stress indices using spectral reflectance and thermal imaging in field crops [8], and MNCFC monitors soil moisture and the Land Surface Wetness Index for drought assessment [20].
Business problem
Micro-irrigation companies, input firms and lenders need to know where water is scarce enough to make efficient irrigation attractive, and where it is so scarce that a crop plan or loan is at risk. Departments need to target subsidies to stressed blocks and verify that installed systems are in use. Groundwater categories are published by block, but they are not joined to cropping patterns, irrigated area or the client's own customers.
Our solution
- Irrigated and rainfed area maps from optical and radar time series, and surface water body mapping.
- Crop water stress layers combining vegetation, wetness and thermal indices, with weekly updates in the season.
- Block-level overlays of groundwater categories, cropping pattern and micro-irrigation coverage for targeting, published on WebGIS.
- Geotagged installation and verification visits in the My GLOBEIR app for subsidy and dealer programmes.
Benefits
- Market and subsidy targeting focused on water-stressed blocks with suitable crops.
- Industry example: Per Drop More Crop already prioritises water-scarce and groundwater-stressed regions [16].
- Industry example: a documented drip and fertigation case raised yield and cut cultivation cost [16].
- Early warning where water stress threatens yield, loan repayment or procurement volumes.
Privacy & data security
- Installation records include beneficiary names, bank-linked subsidy details and plot locations; these are personal data under the DPDP Act [21].
- Water stress analysis runs on area and plot geometry; beneficiary details are held separately with restricted access.
- Verification photos and coordinates are kept only for the retention period agreed with the client.
- Data is encrypted in transit (TLS 1.2 or higher) and at rest (AES-256).
5. Drone Spraying and Field Operations
Drones are now part of India's farm mechanisation policy. The Ministry issued Standard Operating Procedures in 2021 for drone application of pesticides and nutrients, covering flying permissions, area and distance restrictions, registration, pilot certification, operation planning, weather conditions and emergency handling [18], followed in 2023 by crop-specific SOPs for pesticide application [19]. Under the Sub-Mission on Agricultural Mechanisation, institutions can receive up to 100 per cent of drone cost (up to Rs 10 lakh) for demonstrations, FPOs 75 per cent, and custom hiring centres run by cooperatives, FPOs and rural entrepreneurs 40 per cent [19].
The Namo Drone Didi scheme provides 15,000 drones to women Self Help Groups over 2023-24 to 2025-26 with an outlay of Rs 1,261 crore; 500 drones had been distributed under the scheme, and an ADRTC Bangalore study found that adoption had diversified SHG activities and increased income opportunities [13][8]. ICAR institutes are also studying drone spraying systems and droplet deposition to improve the efficiency of pesticide and liquid fertiliser application [8].
A spraying drone is only as precise as its plan. Field boundaries, no-fly and sensitive areas, crop stage and stress zones all decide where and how much to spray, and operators need records of what was applied where.
Business problem
Drone service providers, FPOs and fertiliser companies running drone programmes face scattered bookings across many small plots, travel time between jobs, and a need to prove where and when each spray was done. Without mapped boundaries and stress zones, drones spray whole fields at a flat rate. Without job records, programme managers cannot report coverage or verify subsidy and demonstration claims.
Our solution
- Plot boundary capture and job booking in the My GLOBEIR app, with offline use in the field.
- Stress and nutrient zones from satellite and soil layers to shape spray plans, as in Topic 3.
- Route and schedule planning across bookings, and live tracking of service teams where they opt in.
- Coverage dashboards by village and block on WebMap, with geotagged before-and-after records per job.
Benefits
- Spray plans that follow crop condition rather than whole-field flat rates.
- Industry example: crop-specific SOPs now set the operating framework for drone pesticide application [19].
- Industry example: Namo Drone Didi has created SHG-run drone services with improved income opportunities [13].
- Auditable coverage records for demonstrations, subsidies and client reporting.
Privacy & data security
- Pilot and service team tracking is optional and opt-in in the My GLOBEIR app, with a persistent notification, and for work purposes only.
- Customer bookings hold farmer names and phone numbers; these are visible only to the assigned operator and supervisors.
- Drone survey imagery finer than the DST threshold must be stored and processed in India [24]; GLOBEIR hosts on infrastructure in India or in the client's own environment.
- Job records are retained only as long as the programme or law requires, then exported and deleted.
6. Farm-Level Advisory and FPO Aggregation
Under the Central Sector Scheme, 10,000 Farmer Producer Organisations have been formed, with 56.32 lakh farmers registered as of 1 January 2026 [17]. FPOs aggregate members' produce and input purchases, and they also help farmers register on AgriStack along with Krishi Sakhis and Common Service Centres [15]. Government's own advisory stack is becoming geospatial: the Krishi Decision Support System integrates satellite imagery, weather, soil and crop data in GIS to support targeted crop, weather and soil advisories [13], and Maharashtra has used AgriStack for AI-based advisory as well as for transferring over Rs 14,000 crore to 89 lakh farmers for Kharif 2025 crop losses [13].
For an FPO, a map of members' plots with crop, sowing date, soil and condition turns scattered smallholdings into a single business: it can estimate harvestable volume, plan collection points, buy inputs in bulk for the right crops and give members stage-specific advice. For buyers and agri-input companies, the same map shows which FPOs can supply a given crop and when.
Business problem
Most FPOs keep member and crop records in registers or spreadsheets without plot locations. They cannot forecast volumes to negotiate with buyers, cannot target advisory by crop stage, and struggle to show lenders and partners what their members grow. Companies working with many FPOs have no consistent view across them.
Our solution
- Member plot mapping and seasonal crop updates through the My GLOBEIR app, in the field and offline.
- Crop condition and stage layers per member plot from satellite time series, feeding stage-wise advisory lists.
- Volume forecasting and collection centre planning using road network and travel time analysis from our geospatial data science team.
- Multi-FPO dashboards for agribusinesses, NGOs and programme managers on WebGIS, in the same way as our Digital Micro Mapping work.
Benefits
- Advisory sent by crop and stage, not as a single general message.
- Industry example: the Krishi Decision Support System uses integrated satellite, weather, soil and crop layers for targeted advisories [13].
- Industry example: 10,000 FPOs now provide an aggregation channel reaching over 56 lakh registered farmers [17].
- Volume and quality forecasts that strengthen FPO negotiations with buyers and lenders.
Privacy & data security
- Member records with names, phone numbers and plot locations are personal data; FPOs act as the data fiduciary and GLOBEIR as their processor under contract [21].
- Survey forms can include a purpose notice and a consent record so that the FPO can meet the DPDP requirement that consent be specific and limited to the stated purpose [21].
- Buyers and partners see aggregated volumes by FPO or village, not member-level data.
- Role-based access separates what FPO staff, supervisors and programme partners can see, with audit logs.
7. Yield Estimation and Crop Insurance Support
Crop insurance is the area where Indian policy has gone furthest in using geospatial evidence. The CCE-Agri App uploads crop cutting experiment data directly to the National Crop Insurance Portal, state land records are being integrated with the portal, and YES-TECH introduced technology-based yield with 30 per cent weight for paddy and wheat from Kharif 2023 and soybean from Kharif 2024 [3]. Space inputs now support CCE planning, Gram Panchayat-level yield estimation and resolution of yield and area discrepancies [5]. Earlier protocols for two-step yield estimation use technology to classify losses first, then place more CCEs in moderate or severe areas and fewer in mild or normal ones [6]. Government has also built the Digital General Crop Estimation System for timely crop yield data [14].
For the full insurer view, including claims verification and parametric products, see our guide for insurance.
Business problem
Agribusinesses, lenders and state departments all depend on yield figures: for loan recovery, procurement planning, input demand and claims. Manual CCEs are slow and open to dispute, and organisations that are not YES-TECH implementation partners still need their own objective view of yield and loss in their areas of operation.
Our solution
- Yield proxies and loss categorisation maps from vegetation index time series and weather, for internal planning and risk monitoring.
- Stratification maps that show where crop condition varies, useful for planning sample visits and CCE witnessing.
- Geotagged, time-stamped field and loss records in the My GLOBEIR app.
- Portfolio views for lenders linking crop condition to branch or loan geography, building on our NBFC and microfinance mapping.
Benefits
- Early, objective signal of where yield losses are likely, before final figures are published.
- Industry example: in Kharif 2023, claims in all implementing states were paid on YES-TECH yields, with no disputes reported [4].
- Industry example: two-step estimation focuses CCEs where losses are moderate or severe [6].
- Field evidence that is easier to check because it carries location and time stamps.
Privacy & data security
- Insurance and loan data linked to plots and bank details is personal data; analysis runs on plot or area level without identity wherever possible [21].
- Insurance analysis follows the client's regulatory obligations; see the insurance guide for IRDAI expectations.
- GLOBEIR does not present itself as a YES-TECH implementation partner; formal roles are assigned under the official framework [3].
- Access to and changes in field records are captured in audit logs to support the client's own audits.