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Related Experiment Video

Updated: Aug 8, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
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Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM)

Soo-Hwan Park1, Bo-Young Lee1, Min-Jee Kim2

  • 1Interdisciplinary Program in Smart Agriculure, Kangwon National University, Chuncheon 24341, Republic of Korea.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary

Accurate soil moisture prediction is crucial for crop irrigation, especially with changing climate patterns. This study developed a recurrent neural network long short-term memory (RNN-LSTM) model using weather data to forecast soil moisture, optimizing irrigation for crop growth.

Keywords:
RNN-LSTMdeep learningsmart farmingsoil moisturetime series analysis

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • Climate change impacts soil moisture and crop growth, necessitating precise irrigation strategies.
  • Accurate soil moisture prediction is vital for timely and efficient agricultural water management.

Purpose of the Study:

  • To develop a predictive model for future soil moisture (SM) levels.
  • To inform irrigation decisions based on predicted soil moisture and weather conditions.

Main Methods:

  • Utilized sensors to collect soil moisture and temperature data at multiple depths (10, 20, 30 cm).
  • Developed a recurrent neural network long short-term memory (RNN-LSTM) model using time-series soil moisture, temperature, and weather data.
  • Evaluated model performance using loss and coefficient of determination (R²), validated with two datasets.

Main Results:

  • Achieved high prediction accuracy, with R² values up to 0.999 for 10 cm and 20 cm depths.
  • The RNN-LSTM model demonstrated strong performance in predicting soil moisture in soybean fields.
  • The model effectively correlated soil conditions and weather patterns for predictive insights.

Conclusions:

  • The developed RNN-LSTM model accurately predicts soil moisture, aiding in optimized irrigation scheduling.
  • This approach can be applied to ensure adequate moisture for crop growth, enhancing agricultural sustainability.
  • Time-series weather data integrated into soil moisture prediction models offers significant benefits for crop cultivation.