AI-Based Smart Irrigation System for Precision Agriculture using Soil Moisture Prediction and Weather Data
DOI:
https://doi.org/10.15662/IJEETR.2026.0802223Keywords:
Precision Agriculture, Smart Irrigation, Soil Moisture Prediction, Machine Learning, LSTM, Random Forest, Weather ForecastingAbstract
Efficient water management is one of the most critical challenges in modern agriculture. Traditional irrigation methods often lead to excessive water consumption and reduced crop productivity due to the lack of real-time monitoring and predictive decisionmaking. This paper proposes a multimodal artificial intelligence framework for precision agriculture that integrates soil moisture prediction, weather forecasting, and intelligent irrigation scheduling. The proposed system utilizes Long Short-Term Memory (LSTM) networks to predict soil moisture levels based on environmental data and historical measurements. A Random Forest model is employed to determine optimal irrigation decisions by combining predicted soil moisture values with weather parameters such as temperature, humidity, and rainfall probability. The framework aims to support data-driven irrigation management that improves water-use efficiency and crop yield. Experimental results demonstrate that the proposed approach provides accurate predictions and reliable irrigation recommendations, making it suitable for smart farming applications
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