Toward Sustainable Farming: Implementing Artificial Intelligence to Predict Optimum Water and Energy Requirements for Sensor-Based Micro Irrigation Systems Powered by Solar PV.

Future trends in climate change, water scarcity, and energy costs will motivate agriculturists to develop innovative agricultural systems. In order to achieve sustainable farming in arid regions, there is an urgent need to use artificial intelligence (AI) to predict and estimate the optimum water and energy requirements for the irrigation of date palms. Therefore, this study aimed to predict the optimum water and energy requirements for date palm irrigation depending on the optimum water use efficiency (WUE) and yield in arid conditions. To achieve this aim, four solar-powered micro irrigation systems were developed and evaluated under six irrigation levels for date palm irrigation. Soil moisture sensor-based controllers were used to automate irrigation scheduling for the micro irrigation systems. The water pumping in these systems was powered using a solar photovoltaic (PV) system. In addition, four machine-learning (ML) algorithms, including linear regression (LR), support vector regression (SVR), long short-term memory (LSTM) neural network, and extreme gradient boosting (XGBoost), were developed and validated for prediction purposes. These models were developed in Python programing language using the Keras library. The results indicated that the optimum WUS was achieved when the maximum setpoints of irrigation control were adjusted at the field capacity and by adjusting the minimum setpoints at 40, 50, 70, and 80% of the available water (AW). The optimum yield was achieved by adjusting the minimum setpoints at 60, 70, 80, and 90% of AW for subsurface irrigation, subsurface drip irrigation, drip irrigation, and bubbler irrigation, respectively. Therefore, the dataset was prepared at these levels for four years to train and test the models, and a fifth year was used to validate the performance of the best model. The evaluation of the models showed that the LSTM followed by XGBoost models were more accurate than the SVR and LR models for predicting the optimum irrigation water and energy requirements. The validation result showed that the LSTM was able to predict the water and energy requirements for all irrigation systems with R2 ranging from 0.90 to 0.92 based on limited meteorological variables and date palm age. The findings of the current study demonstrated that the developed LSTM model can be a powerful tool in irrigation water and energy management as a fast and easy-to-use approach. [ABSTRACT FROM AUTHOR]