Remote Sensing for
Precision Farming

Remote sensing uses satellite and drone imagery to deliver accurate crop classification, crop health monitoring, and predictive yield estimation through advanced AI, ML models and biophysical analysis.





Crop Classification

Remote sensing uses satellite and drone imagery for accurate crop identification, classification and crop area estimation over large areas, crop health monitoring, and predictive yield estimation through advanced AI, ML models and biophysical analysis.

Area Estimation
Precisely determine crop area and distribution
Crop Mapping for Insurers
Understanding crop distribution in specific areas
Seasonal Analysis
Monitor seasonal changes in crop patterns and understand long-term trends

Crop Health Monitoring

Tracks crop conditions using spectral indices such as: NDVI, GNDVI, NDRE, LAI, LSWI, and SAR-based (RVI, DPRVI) in an integrated manner for precise assessments.

Early Warning Systems

Detection of risk areas early on for timely intervention in the event of pest / disease infestation, drought etc.

Field Advisory Services

Insights for irrigation and nutrient requirements.


Input Optimization

Identification of problem zones for precise fertilizer and pesticide usage.

Yield Estimation

Fuses remote sensing (PAR, FAPAR), weather, soil, biophysical & SAR data (VV, VH), and AI/ML methods (Semi-Physical, DSSAT) to forecast pre-harvest crop output.

Disaster Forecasting Forecasts losses from adverse weather or pest outbreaks
Ensemble Modelling Combines multiple models with AI/ML for robust, accurate yield predictions
Scenario Simulation Models yield outcomes under varied climatic and management scenarios
Planned Agriculture Increase efficiency through planned procurement and logistics
Claims & Loss Analytics Data-driven yield estimates for insurance claims and loss assessments
Yield Estimation