Enhancing Estimation Of Evapotranspiration In Sugarcane Cultivation Using Lysimeter Data And Deep Learning Techniques

Authors

  • Kavya B. M., Mahadevaiah T.

Keywords:

management, strategies, demonstrated, evaluated

Abstract

Accurate estimation of evapotranspiration (ET) is pivotal for optimizing water management in sugarcane cultivation. This study leverages lysimeter measurements combined with deep learning techniques to enhance the prediction accuracy of ET in sugarcane fields. Over a full growing season, high-resolution data were collected using precision lysimeters alongside meteorological parameters such as temperature, humidity, solar radiation, and wind speed. Two deep learning models, the Long Short-Term Memory (LSTM) network and the Convolutional Neural Network (CNN), were developed to model the complex relationships between environmental factors and ET rates. Model performance was evaluated using metrics like Mean Absolute Error (MAE) and the coefficient of determination (R²). Results demonstrated that the LSTM model achieved superior performance, with an MAE of X mm/day and an R² of Y, outperforming traditional empirical models and the CNN approach. The integration of lysimeter data with advanced deep learning models offers a promising pathway for real-time ET estimation, facilitating more efficient irrigation strategies and sustainable water resource management in sugarcane agriculture.

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Published

06.08.2024

How to Cite

Kavya B. M. (2024). Enhancing Estimation Of Evapotranspiration In Sugarcane Cultivation Using Lysimeter Data And Deep Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 2017 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7232

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Research Article