Multi-Model AI-Based Smart Irrigation System for Precision Water Management in Agriculture

computer engineering

Irrigation plays a vital role in sustainable crop production, yet conventional practices often rely on fixed thresholds or manual control, which leads to excessive water consumption, nutrient loss, and reduced efficiency. With climate variability and growing water scarcity, there is an urgent need for intelligent and adaptive irrigation strategies. This project proposes an AI-driven irrigation scheduling system that integrates Internet of Things (IoT) sensors, pumps, and predictive machine learning models to achieve precision irrigation. Soil moisture, humidity, and temperature data are continuously monitored and transmitted to a cloud platform, where artificial intelligence algorithms analyse real-time conditions along with historical patterns and weather forecasts. Based on this analysis, the system predicts crop-specific water requirements and schedules irrigation at the most effective intervals, thereby minimizing wastage and ensuring optimal soil conditions. The integration of AI enables the system to dynamically adjust to seasonal changes, rainfall events, and soil type variations, unlike conventional threshold-based systems. Remote access through mobile and web applications with voice assistant allows farmers to monitor and control irrigation operations from anywhere, improving ease of use and reliability. The proposed model not only conserves water and reduces energy costs through optimized pump operation but also enhances crop yield and resilience to climatic stress. By merging predictive analytics with renewable energy and IoT-based automation, this system represents a significant step toward smart, autonomous, and climate-resilient agriculture.

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