Sensor-Based Monitoring Systems for Agricultural Water Management

Agricultural water management depends on timely and accurate field information, yet irrigation in many settings still follows fixed schedules or isolated sensor readings that do not reflect changing soil and environmental conditions. This paper presents a sensor-based monitoring framework for agricultural water management that combines zone-level field sensing, communication-supported data collection, irrigation priority analysis, and node-reliability assessment within a single decision structure. The proposed system monitors soil moisture, soil temperature, air temperature, relative humidity, water flow, and source water level across multiple field zones. At the same time, it evaluates battery status, signal strength, and packet stability so that technical faults are not interpreted as crop water stress. The methodology includes a multi-layer system architecture, zone-based deployment, data filtering, irrigation scoring, and reliability screening before alert generation. Results show that the framework supports zone-specific irrigation guidance, reduces fault-related false alerts, and improves decision quality compared with fixed-schedule irrigation practice. The system also provides more consistent temporal response through trend-based monitoring and maintenance-aware alert logic. These findings indicate that agricultural water monitoring is more dependable when field condition and node condition are assessed together. The proposed framework offers a practical model for data driven irrigation monitoring in remote and resource-constrained agricultural settings, with future scope for automated control and crop-specific decision support.