Machine Learning Approaches for Crop Water Requirement Prediction and Optimization

This review provides insights into the use of machine learning (ML) techniques for predicting and scheduling crop water requirements (CWR) in agricultural water management. With climate change and increasingly demanding populations, precision irrigation is now more than ever an imperative for food security and sustainable agriculture. This paper provides a comparison of different ML models in estimating the CWR: Random Forests, Support Vector Machines, deep learning, and hybrid models. According to reviewed literature, prediction precision above 90% and water savings ranging from 30% to 50% were achieved with ML systems when compared to traditional methods. Hybrid models such as CNN-LSTM and SVM-DT combinations achieve better results, mainly due to their capacity to capture rich spatiotemporal patterns. Additionally, adding IoT sensors, remote data, and meteorological parameters enhances the model's efficiency to manage irrigation in real time. The benefits of these improvements lead to input costs being 10-20% lower, yields are typically improved by 15-25%, CO2 emissions are reduced, and pesticides are used far less frequently, between 30 – 40%. The use of AI has demonstrated diverse promising applications, although practical difficulties such as data quality, computational requirements, and the fusion with traditional practices still exist. This work provides a direction line to follow research in federated learning, explainable AI, transfer learning, and edge computing. ML Technologies, as in every field, represent a revolutionary development in agricultural water management that provides vital means of sustainable agriculture and water saving.