Our trained field agents visit the field and capture geolocation of the plot, take photographs and crop health information using the specially designed ‘CropTech’ App. This data is made available to the clients through a specially designed dashboard.
Drone is basically a flying object. Survey drones are fitted with special sensors to gather information about the crop in the form of multispectral images. These images are then processed to extract parameters such as crop health, extent of loss, expected yield etc. Spraying Drones are fitted with tank and nozzles to spray liquids or broadcast powders and granules.
Depends on capacity of the tank fitted, battery and the particle size. On an average, a drone can cover one acre area in about 8 to 10 minutes. Multiple battery sets are used to increase the flying time.
Various attributes of an individual plot can be stored and analysed in GIS environment to aid decision making by the farmer. This information includes ownership details, area, soil attributes, crop profile, management practices followed, irrigation etc.
Remote sensing adds immense value in the form of spectral information. Judging crop health, occurrence of pests and diseases, detection of moisture stress, estimation of yield etc. is possible using the remote sensing images. Timely and exact detection of problem areas enables precise control measures thereby saving costs and increasing yield.
GIS provides power to handle multiple attributes and enables complex queries making decision making more accurate and fast. Remote sensing adds a different perspective in the form of visual information about the plot capable to cover large areas in less time.
Insurance companies use the crop monitoring data for insured area verification, estimation of losses and predicting yield to estimate claims. Crop monitoring information is used to generate advisory for the farmers leading to efficient crop management.
Crop health data includes various indices which correlate to chlorophyll percentage, biomass, canopy coverage etc. This information, coupled with ground observations and historical data can be modelled to estimate crop yield.