Karnataka is planning to use satellite imagery, drone data and artificial intelligence (AI) to monitor crop health and estimate agricultural yields, particularly in drought-affected regions. The Karnataka State Remote Sensing Applications Centre (KSRSAC) has invited bids from companies, agencies and startups to develop a monitoring system under the CM Raitha Chaitanya Karnataka Agritech Mission 2026. The pilot will cover two agricultural seasons, Rabi 2026–27 and Kharif 2027–28, before the state considers a wider rollout.
The initiative comes as Karnataka faces severe agricultural distress. The state government has declared 226 of its 240 taluks drought-hit, with reported crop losses exceeding ₹46,324 crore. The proposed system will combine satellite observations, weather information, geographic data and field inspections to identify areas where crops may be deteriorating. Officials hope the technology will improve crop-loss assessments and village-level yield estimates, helping the government make better-informed decisions about agricultural planning and relief.
Key Takeaways
- New monitoring system: Karnataka plans to combine satellite imagery, drone data, weather information and AI to monitor crops.
- Pilot duration: Testing is planned across Rabi 2026–27 and Kharif 2027–28.
- Coverage: Two study areas of approximately 500 square kilometres each will be monitored.
- Drought context: The state has declared 226 of 240 taluks drought-hit, with reported crop losses exceeding ₹46,324 crore.
- AI applications: The system is intended to classify crops, monitor crop health, identify potential pest and disease outbreaks, and estimate yields.
- Fortnightly updates: Crop-health assessments and maps are expected every two weeks.
- Statewide expansion: A wider rollout will depend on pilot performance, accuracy, approvals and procurement decisions.
Why Karnataka Is Turning to Satellite-Based Crop Monitoring
Agriculture is highly sensitive to changes in rainfall, temperature, soil moisture and pest activity. When drought conditions persist, farmers can experience reduced yields, crop failure and higher production costs. Governments must also determine which areas have suffered losses and where assistance may be needed.
Traditional crop assessments often depend on field visits, local reports and crop-cutting experiments. These methods remain important, but they can require considerable time and coordination across large agricultural areas.
Satellite-based monitoring offers another source of information. Images captured from space can help officials observe changes in vegetation, identify unusual patterns and compare crop conditions across locations. When combined with ground observations and weather data, these signals can support more consistent assessments.
Karnataka’s proposed system is intended to provide a regular picture of agricultural conditions rather than relying solely on observations collected at particular points in the season. This could help officials identify emerging crop stress earlier and decide where additional field inspections are needed.
The system will not independently determine every cause of crop damage. Satellite observations must be interpreted alongside local conditions, crop varieties, soil characteristics and information collected on the ground.
Which Areas Will the Pilot Cover?
The pilot is expected to cover two study areas, each measuring approximately 500 square kilometres. The project will run across the Rabi 2026–27 and Kharif 2027–28 crop seasons, allowing the technology to be tested under different agricultural conditions.
| Study area | Locations included |
|---|---|
| Area 1 | Athani taluk in Belagavi district and Tikota in Vijayapura district |
| Area 2 | Devadurga and Sindhanur taluks in Raichur district, and Kustagi taluk in Koppal district |
| Total planned coverage | Approximately 1,000 square kilometres |
| Pilot seasons | Rabi 2026–27 and Kharif 2027–28 |
The selected locations will allow the state to test whether the system can monitor crops across different districts and agricultural settings. Running the pilot through two seasons should also provide more opportunities to compare satellite observations with field-level crop conditions.
KSRSAC has invited technology providers to submit bids for developing and demonstrating the system. The pilot is therefore a planned project, not a completed statewide deployment. Its performance and accuracy will help determine whether the approach can be expanded.
How Satellites, Drones and AI Will Work Together
The proposed platform will combine several types of information to build a more detailed view of crop conditions.
Satellite imagery
Optical satellite imagery can help identify vegetation patterns and changes in crop growth. High-resolution imagery may allow analysts to distinguish agricultural areas and assess how vegetation changes over time.
The project will also use synthetic aperture radar (SAR) imagery. Unlike optical imaging, radar can collect useful observations in many cloudy conditions, making it particularly valuable during monsoon periods when cloud cover may limit the availability of clear optical images.
Drone-based observations
Drone imagery may supplement satellite data when additional detail is needed. It could be particularly useful when cloud cover prevents the collection of suitable optical satellite images during the Kharif season.
Drones can capture detailed views of selected fields, but their use requires planning, permissions where applicable and coordination with field teams. They are intended to complement the wider monitoring system rather than replace satellite observations.
Weather and geographic data
Weather information can help analysts interpret changes in crop health. Rainfall patterns, temperature and other meteorological conditions can influence how crops develop and whether they experience stress.
Geographic information system (GIS) datasets will provide spatial context, allowing officials to connect crop observations with village boundaries, agricultural areas and other mapped features.
Artificial intelligence and machine learning
AI and machine-learning models are expected to classify crops, monitor crop health and identify possible pest or disease outbreaks. They will also help generate crop-wise statistics and support estimates of agricultural production.
The system is expected to generate crop-health maps every fortnight. Areas showing abnormal or declining crop conditions could then be flagged for further investigation.
These are intended capabilities, not a guarantee that the technology will identify every crop problem correctly. Models must be tested against real-world observations to determine how reliably they perform across different crops, weather conditions and locations.
How the Technology Could Improve Crop-Loss Assessments
One of the proposed system’s most important applications is improving the evidence available for crop-loss assessment.
When drought or another agricultural disruption damages crops, officials need to estimate the extent of the damage and identify the areas affected. These assessments can inform planning, relief decisions and other government interventions.
Satellite imagery can provide broad coverage, while drone observations and field inspections can help verify conditions at selected locations. AI models can then combine these inputs to identify patterns and estimate the severity of crop stress.
The project also aims to develop validated models for estimating crop yields at the village level. These estimates will draw on satellite and drone imagery, weather information, crop-growth parameters, field observations and crop-cutting experiments.
Village-level estimates could help officials compare agricultural performance across locations and identify areas where yields may be significantly below expectations.
However, remote sensing cannot replace every field assessment. A change in vegetation signals may indicate drought stress, disease, harvesting or another factor. Field verification remains important before estimates are used to make consequential decisions about compensation or relief.
Why the Project Matters During Karnataka’s Drought
The scale of Karnataka’s agricultural distress makes better crop information particularly important. Reports published in October 2026 put the number of drought-hit taluks at 226 out of 240, with crop losses estimated at more than ₹46,324 crore.
Drought can affect not only farmers’ harvests but also local agricultural supply chains, rural incomes and demand for farming inputs. More timely information about crop conditions could help authorities identify emerging problems and plan their responses.
A monitoring system that produces regular assessments could also help officials compare the development of drought conditions across regions. Rather than relying on a single snapshot, they could track changes through successive observations.
Still, the system’s usefulness will depend on the quality of its data and the speed with which findings can be converted into practical action. Better monitoring alone does not provide water, compensate for crop losses or guarantee that assistance reaches affected farmers.
What Happens After the Pilot?
KSRSAC intends the system to be scalable, potentially allowing it to cover more agricultural areas if the pilot demonstrates reliable results.
The state will need to evaluate several factors before expanding the programme. These include the accuracy of crop classification, the reliability of yield estimates, the usefulness of fortnightly maps and the ability to validate AI-generated assessments against field data.
The cost of collecting and processing imagery, maintaining the technology and coordinating ground inspections will also matter. A system that produces technically impressive maps must still be practical for officials to use regularly.
The tender process will determine which providers participate in developing the platform. Any broader deployment will depend on the results of the pilot, approvals and subsequent procurement decisions.
The Bigger Picture
Karnataka’s proposed project reflects a broader shift towards using remote sensing and AI to support agricultural decision-making. Satellites can provide a wide-area view, drones can offer closer inspection, and machine-learning models can help turn large volumes of data into crop classifications and estimates. Combined with field verification, these tools may improve how governments monitor agricultural conditions.
The key test will be whether the system produces sufficiently accurate and timely information to support real decisions. If the pilot succeeds, Karnataka could have a model for expanding data-driven crop monitoring. If the results are inconsistent, the state will need to refine the technology and its validation methods before relying on it at a larger scale.
Looking Ahead
The pilot is planned across the Rabi 2026–27 and Kharif 2027–28 seasons, with fortnightly crop-health assessments and village-level yield estimation among its intended outputs. The next milestones will be the selection of technology providers, implementation of the monitoring system and comparison of its estimates with field observations and crop-cutting experiments.
For farmers, the potential benefit lies in better evidence about where crops are under stress and how agricultural losses are developing. Whether that translates into more effective relief or planning will depend on how accurately officials can interpret the data and integrate it into existing agricultural programmes. Statewide expansion remains a possibility rather than a confirmed outcome.
Frequently Asked Questions
1. Why is Karnataka using satellite technology to monitor crops?
The state wants to improve crop-health monitoring, drought assessment and yield estimation by combining satellite imagery with weather data, drone observations and field inspections.
2. Which areas will the pilot cover?
The two study areas include Athani and Tikota, along with Devadurga, Sindhanur and Kustagi taluks across Belagavi, Vijayapura, Raichur and Koppal districts.
3. When will the pilot take place?
The planned pilot covers Rabi 2026–27 and Kharif 2027–28.
4. How will AI help farmers?
AI models are expected to classify crops, flag possible crop-health problems and support village-level yield estimates. These outputs could help authorities identify areas requiring further assessment, although they do not guarantee direct assistance to farmers.
5. Can satellites accurately identify all crop diseases?
No. Satellite observations can identify patterns associated with crop stress, but those patterns may have several causes. Field inspections and other evidence are needed to validate suspected pest or disease outbreaks.
6. Will Karnataka immediately roll out the system statewide?
No. The initial project is a pilot. Any wider rollout will depend on its performance, accuracy, approvals and procurement decisions.
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