Growing population of India has an increased need for food, energy, and water, which calls for organized water management with improved crop yield. Accurate estimation of evapotranspiration (ET) is the first step in evaluating the water requirement of field crops. The solar radiation data are essential for estimating the reference crop ET (ET0). Due to the cost and difficulties in direct measurement techniques, solar radiation and ET0 were predicted using random forest machine learning (ML) and empirical methods. The water requirement for the tomato crop in millimetres/day is calculated using the estimated ET. Meteorological parameters associated with this study were obtained from the Indian meteorological department and the AQUASTAT tool. Based on the performance metrics such as MSE the value of 0.03 and correlation coefficient of 0.97, it is observed that solar radiation and ET0 predictions using random forest ML are better than the empirical model. Thus, this climate-smart agriculture approach can be applied as a successful strategy for irrigation planning in intelligent farming.
Key words: Evapotranspiration, Machine learning, crop water, solar radiation, Random Forest.
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