Modeling Evapotranspiration in IoT based WSN for Irrigation Scheduling: An Optimized DL Approach

The scarcity of freshwater resources throughout the world has raised the demand for the optimal utilization of these resources. Internet-of-things (IoT) based wireless sensor networking is an interesting and vital technology that has seen substantial growth in recent years. Agricultural applications are one of the fields where it is extensively used and effectively implemented to manage irrigation requirements. In smart agriculture, reference evapotranspiration (ET0) has a great significance in determining the precise crop water requirement. Hence, accurate estimation of ET0 is critical to avoid under or over-irrigation without compromising the agricultural productivity. This paper presents Genetic algorithm (GA) based optimized Long short term memory (LSTM) model (LSTM-GA) to estimate reference evapotranspiration (ET0) using climate data acquired by IoT based wireless sensor networks (WSN). Specifically, we examine the temporal property of climate data by proposing a systematic way of determining the time window size for the LSTM model using Genetic algorithm. The climate variables include daily maximum temperature (Tmax), minimum temperature (Tmin), solar radiation (Rs), sunshine hours (SSH), wind speed (U2), relative humidity (Rh), and vapor pressure (Vp) of Ludhiana station. The proposed hybrid model was validated against the benchmark technique FAO-PM for estimating ET0. The performance comparison revealed that LSTM-GA provided reliable results and outperformed the stand-alone LSTM model