Improving Irrigation Scheduling through Deep Learning-Based Reference Evapotranspiration Estimation

Praveen Kumar Khandappa; Manjula Sunkudkatte Haladappa

doi:10.48084/etasr.15002

Authors

Praveen Kumar Khandappa Department of Computer Science and Engineering, University of Visvesvaraya College of Engineering, Bangalore University, Bengaluru, India
Manjula Sunkudkatte Haladappa Department of Computer Science and Engineering, University of Visvesvaraya College of Engineering, Bangalore University, Bengaluru, India

Volume: 15 | Issue: 6 | Pages: 30185-30190 | December 2025 | https://doi.org/10.48084/etasr.15002

Received: 22 September 2025 | Revised: 9 October 2025 | Accepted: 15 October 2025 | Online: 25 November 2025

Corresponding author: Praveen Kumar Khandappa

Abstract

Agricultural water management is one challenging issue for farmers. The estimation of accurate crop watering plays a vital role in improving yield and water management. Reference Evapotranspiration (ET0) is a weather-based parameter that can help estimate crop water requirements. This study investigated Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models for ET₀ prediction. The proposed ANN model achieved a Mean Squared Error (MSE) of 0.1246 and an R-squared (R²) of 0.9588. The CNN model was limited by the lack of spatial patterns, and the LSTM did not perform as well as it could due to minimal sequential dependencies. The results of this study show the importance of aligning model architecture with dataset characteristics.

Keywords:

agricultural water management, reference evapotranspiration (ET0), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), crop water requirement prediction

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