A Novel Machine Learning Model for Dynamic Irrigation Scheduling in Water-Scarce Regions

Abstract

Modern agriculture, especially in areas experiencing scarcity of water and varying climatic conditions, is dependent on effective irrigation management. In this study, a new model of an irrigation system has been designed by the author. This model uses the best possible prediction of machine learning algorithms so that wastage of water resources may be avoided by predicting the irrigation schedule. The inputs to this model are soil moisture data, weather prediction, and crop-specific parameters, which generate personalized irrigation recommendations. A novelty of this paper lies in its design of such a system. The model's key strength lies in its adaptability to different agricultural environments, such that it may be widely applied. Methodology The data gathering, feature engineering, model training, and evaluation form the core of the methodology. Results Comparisons with the traditional approach present improvements in irrigation efficiency, where a significant amount of water saved and higher crop yield have been observed. Scalability and real-time decision making are promising prospects for precision agriculture. Future work includes expansion of the scope of the system towards adding more integrate with other farming technologies for a more complete smart farming solution and be influenced by environmental factors.