Soil moisture prediction is a key parameter for effective irrigation scheduling and water use efficiency. However, accurate long-term prediction remains challenging, as most existing models excel in short- to medium-term prediction but struggle to capture the complex temporal dependencies and non-linear interactions of soil moisture variables over extended horizons. This study proposes a hybrid soil moisture prediction method, integrating a long short-term memory (LSTM) network and extreme gradient boosting (XGBoost) model for multistep soil moisture prediction at 24 h, 72 h, and 168 h horizons. The LSTM captures temporal dependencies and extracts high-level features from the dataset, which are then used by XGBoost for final predictions. The study uses 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 on the farm. Results show that the proposed method outperforms other hybrid models, achieving R² values of 98.67%, 98.54%, and 98.56% for 24, 72, and 168 h predictions, respectively. The study findings highlight that LSTM-XGBoost offers a precise long-term soil moisture prediction, making it a practical tool for real-time irrigation scheduling, enhancing water use efficiency in precision agriculture.

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.