Hybrid LSTM Method for Multistep Soil Moisture Prediction Using Historical Soil Moisture and Weather Data

Soil moisture is a key parameter in agriculture. Its accurate prediction is essential for effective irrigation scheduling and water use efficiency. This study introduces a hybrid approach integrating Long Short-Term Memory (LSTM) network and Extreme Gradient Boosting (XGBoost) model for multistep soil moisture prediction for (24 hours, 72 hours, and 168 hours) ahead. The LSTM captures temporal dependencies and extracts high-level features from the dataset, while XGBoost uses these features to make final predictions. The proposed method was trained and evaluated on a real-world data from the D.A.T.A (Demonstrating Applied Technology in Agriculture) research farm at ABAC (Abraham Baldwin Agricultural College) Tifton, GA, USA, utilizing watermark soil moisture sensors and weather station’s data installed in the farm. Experimental results show that, the pro-posed method outperforms standalone models like LSTM, XGBoost, Gradient Boosting (GB), Extra Trees (ET) and others. It achieved R² values of 0.9867, 0.9854, and 0.9856 for 24, 72 and 168-hour predictions respectively. These results demonstrate the effectiveness of LSTM in extracting high-level temporal features, and XGBoost in making final predictions. The proposed hybrid model offers precise soil moisture prediction, making it a practical tool for real-time irrigation scheduling and enhancing water use efficiency in precision agriculture.