Local weather interpolation using remote AWS data with error corrections using sparse WSN for automated irrigation for Indian farming

Abstract

Automated irrigation system needs weather information for irrigation control. Scattered automated weather stations (ASW) from government agencies or wireless sensor network (WSN) are used for weather monitoring purpose. Each has its own advantages and disadvantage in terms of cost to farmers and accuracy on monitoring parameters. This paper proposes a technique of real-time spatial interpolation using nearby ASW to predict real-time local weather (area under consideration for irrigation) parameter and accuracy of result is about 99.59%. Further, this paper proposes a correction technique by using sparse WSN with soil moisture sensor installed in it. This proposed technique is expected to increase the accuracy of climatic parameters for the area under consideration with more precise irrigation, which in turn saves energy, water and installation cost to farmers.