A Machine Learning-based Smart Water Management Framework for Agricultural Irrigation using a Stacked Ensemble Model

Employing SWM in agricultural irrigation enhances efficiency, reduces costs, and boosts crop yield, while simultaneously benefiting the environment. We can now accurately quantify the water requirements of each plant, thanks to technological breakthroughs. The harmonious integration of diverse technologies poses a significant barrier to efficient solid waste management. Agriculture utilises around 70% of global freshwater, making effective water management essential for maintaining a sustainable food and water supply. In their quest for optimal irrigation, they utilised RF, RFE, and SelectKBest for feature selection after processing features with blanks. The ideal irrigation timetable for plants was established using a stacking ensemble technique. The recently constructed EM-MoNet network also facilitated SWM. This model utilised a two-stage transfer learning methodology that included re-training with the target dataset to improve its accuracy. The proposed method shown utility with an accuracy of 98.23% on a local dataset. This study highlights the importance of SWM in precision irrigation, which enhances sustainable agriculture by augmenting crop yields and reducing water consumption.