Machine Learning-Based Intelligent Water Management System for Agriculture

Agriculture depends on reliable access to water, yet irrigation decisions are often affected by changing weather, soil variability, cropgrowth stages, and limited monitoring. This paper proposes a Machine Learning-Based Intelligent Water Management System forAgriculture that combines soil-moisture observations, weather information, crop and soil profiles, and irrigation history to supporttimely water-management decisions. The proposed design uses a sensor and controller layer to collect and validate data, amachine-learning module to estimate short-term water need, and a safety layer to determine whether irrigation should berecommended or activated. The system includes rainfall checks, sensor-fault handling, maximum pump-runtime limits, manualoverride, and post-irrigation feedback. A rule-based threshold controller is retained as a transparent baseline and fallback whenmodel confidence is low or connectivity is unavailable. The paper outlines system architecture, a conceptual algorithm, candidatehardware and software, evaluation metrics, limitations, and future development. This is a design-oriented paper; it does not claimmeasured water savings, yield improvement, or field-test results. Such benefits must be established through controlled anddocumented field evaluation.