Hourly Real-Time Rainfall Estimation for Improved Smart Irrigation System Using Nearby Automated Weather Station

Abstract

Smart irrigation is done by extracting climatic data such as historical data, off-site data, weather station, moisture sensor, wireless sensor network and web-based forecast. In existing sensor-based smart irrigation schedule, the decision-making of current irrigation depends on the current climatic data.  Irrigation control decision making systems can be improved by using neighborhood real-time rainfall for approximate local rainfall estimation. This method can result in better water saving techniques. This paper shows the development of low-cost smart irrigation system which consists of Automatic Weather Station (AWS), Central Irrigation Control Server, wireless modules, soil moisture sensors and solenoid values. For improved decision making an artificial neural network with back-propagation algorithm is implemented to estimate real-time hourly rainfall by using nearby AWS. Depending on the estimated rainfall input, the irrigation decision can be immediate irrigation if no rainfall or reschedule of irrigation for next cycle if expecting sufficient amount of rainfall or may be partial irrigation for insufficient rainfall. This method can utilize rainfall for fields and saves ground water resources. This method also avoids flooding and damage to crop due to significant rainfall just after scheduled irrigation. Avoiding of flooding is very curial especially in germination period of any crop. In study area of NCMRWF, National Capital Region (NCR) on particular day of 22nd and 23rd Jan 2015 continuous rainfall of 152 mm of record, shows that for irrigation area of 1000 m2we can save up to 1,52,000 litre of fresh water by using real-time rainfall estimation technique. This technique can save ground/reservoir water resources in arid and semi-arid regions like India.