Deep Learning Approach for Water Management in Agriculture

Abstract

Water is an indispensable natural commodity, a fundamental human need, and the most critical source for the existence of the human race and its development. Presently, the world is grappling with water scarcity issues which have risen to an alarming mark. Climate change projection shows that the situation will become grimmer and more prevalent in the near future. The agriculture sector is the primary consumer of water and uses almost 75-80% of the freshwater available worldwide. India, an agricultural country, requires a huge amount of water for irrigation. Still, it has only 4% of the world s freshwater to serve the food demands of about 18% of the global population. Thus, precise estimation of the irrigation requirements is a significant task to manage the available water resources efficiently and optimize water usage in agriculture. Researchers are trying hard to develop efficient irrigation techniques. The water requirement problem requires more efficient, intelligent, and sustainable techniques to address the problem. Evapotranspiration (ET) is a significant factor in determin- ing crop water requirements. The precise estimation of reference evapotranspiration (ET0) and crop evapotranspiration (ETc) is necessary to determine the irrigation requirements to maintain crop-water balance. Deep learning (DL) models are effi- cient in handling complex non-linear relationships with a massive amount of data. It shows the ability to handle time-series forecasting problems. Estimating evapo- transpiration shows similar characteristics, such as complex non-linear relationships among meteorological parameters and the time-dependence nature of these param- eters. Therefore, the work reported in this thesis is carried out to develop deep learning- based models for the precise estimation of ET0 and ETc values. These proposed DL models are further investigated to handle the limited availability of meteorological data required for their reliable estimation. The main contributions of this thesis are as follows: