Smart Agriculture

Traditionally agriculture is practiced by performing a particular task, such as planting or harvesting, against a predetermined schedule. But by collecting real-time data on weather, soil and air quality, crop maturity and even equipment and labor costs and availability, predictive analytics can be used to make smarter decisions.


Decision support, actionable advise, specific steps to follow, arising out of data analysis and variability of data tracking, crop modeling, short-term and long-term action plans, tree volume and count, anomaly determination, hyperlocal weather forecasting, pest infestation risk assessment, resource optimization.



Monitoring of moisture, temperature, humidity, wind-speed, wind direction, solar and ultraviolet radiation, soil moisture, electrical conductivity, soil temperature, stem, fruit, trunk, diameter through in-situ sensors, land cover mapping, spectral sensing, reflectance sensing through Satellite & UAV. Anytime, Anywhere.

Mapping & Aerial Surveys

The adoption of satellite imagery has expanded significantly with the improvement of resolution, spectral signatures and coordinate accuracy. Further, extensive archives of satellite data support in-time acquisition and delivery of data in mono and stereo modes. Today, one can obtain rectified images of 30cm resolution, sufficient for most agricultural status monitoring & tracking applications.


Hyperlocal Weather

Hyperlocal weather or Microscale Metrology is a combination of influence of various parameters including Wind Speed, Wind Direction, Temperature and humidity, Photosynthetically Active Radiation, Atmospheric Pressure, and Precipitation through sensors at the particular spot of interest.

Yield Estimation

Every process of yield estimation has two main phases of development. The first phase is the calibration of the method and the second phase is the operational application. Ground-truth data are used in the first phase; however, during the operational phase ground data is not required. Both phases require satellite remote sensing data over a period of time and takes the crop growth models to form the basis of the development.


Pest Infestation

If pest infestation is an important consideration for annual losses of large and small farm holders, Remote Grid can undertake studies to develop a model for early warning systems to warn farmers of the potential onset of certain types and strains of pests.

It is possible to study which conditions of temperature, humidity and light allow a certain pest to thrive and what parameters to monitor to create an advanced warning system of the likely onset of any infestation aided by favorable weather conditions.