First published on October 20, 2018

With the increase in population, demand for food is also increasing. The critical challenges in attaining food security include the adverse climatic changes and diminishing agricultural yield due to several factors such as shortage of water, pests, and other diseases. The prediction of crop yield in near real-time helps to tackle adverse climatic changes and reduce its effect on crop yield. The knowledge about the variation of these changes with time is of utmost importance as this helps to adapt different agricultural practices for the betterment of yield. With the emergence of satellite sensors, many parameters influencing agricultural yield can be monitored and analyzed. Agro-hydrological variables influencing agricultural yield such as Soil Moisture, rainfall, vegetation indices and evapotranspiration are used for the estimation. Detecting how these parameters vary over time helps to predict the changes well before its occurrence and thus provides a possibility of preparedness without compromising on the yield.

Many times, the spatial-temporal variation of yield is observed in the same agriculture land because of the spatiotemporal variation of soil structure and properties. As water remains one of the limiting factors in Indian agriculture, calculating irrigation requirement of the soil types spatiotemporally would be advantageous. Proper usage of water can increase the yield and crop health. Precision irrigation is the practice of irrigating the crops with the optimal amount of water.

High-resolution soil moisture data calculated using the innovative algorithm developed by Satyukt Analytics Pvt Ltd is used to determine the optimal amount of water required for each crop based on the soil type and properties. Irrigation requirement varies with time and hence near real-time monitoring of the soil moisture, soil type and other agro-hydrological parameters such as evapotranspiration, rainfall, vegetation index etc would help to determine the amount of irrigation required at various seasons.