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A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer

Author

Listed:
  • Junrui Pan

    (College of Electronic Engineering & College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China)

  • Long Yu

    (College of Electronic Engineering & College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China)

  • Bo Zhou

    (Tea Research Institute, Guangdong Academy of Agricultural Sciences & Guangdong Provincial Key Labora-tory of Tea Plant Resources Innovation and Utilization, Guangzhou 510640, China)

  • Junhong Zhao

    (Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China)

Abstract

Daily reference crop evapotranspiration (ET 0 ) is crucial for precision irrigation management, yet traditional prediction methods struggle to capture its dynamic variations due to the complexity and nonlinearity of meteorological conditions. To address this, we propose an Improved Informer model to enhance ET 0 prediction accuracy, providing a scientific basis for agricultural water management. Using meteorological and soil data from the Yingde region, we employed the Maximal Information Coefficient (MIC) to identify key influencing factors and integrated Residual Cycle Forecasting (RCF), Star Aggregate Redistribute (STAR), and Fully Adaptive Normalization (FAN) techniques into the Informer model. MIC analysis identified total shortwave radiation, sunshine duration, maximum temperature at 2 m, soil temperature at 28–100 cm depth, and surface pressure as optimal features. Under the five-feature scenario (S3), the improved model achieved superior performance compared to Long Short-Term Memory (LSTM) and the original Informer models, with MAE reduced to 0.065 (LSTM: 0.637, Informer: 0.171) and MSE to 0.007 (LSTM: 0.678, Informer: 0.060). The inference time was also reduced by 31%, highlighting the enhanced computational efficiency. The Improved Informer model effectively captures the periodic and nonlinear characteristics of ET 0 , offering a novel solution for precision irrigation management with significant practical implications.

Suggested Citation

  • Junrui Pan & Long Yu & Bo Zhou & Junhong Zhao, 2025. "A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer," Agriculture, MDPI, vol. 15(9), pages 1-21, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:9:p:933-:d:1642062
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    References listed on IDEAS

    as
    1. Umutoni, Lisa & Samadi, Vidya, 2024. "Application of machine learning approaches in supporting irrigation decision making: A review," Agricultural Water Management, Elsevier, vol. 294(C).
    2. Konstantinos Dolaptsis & Xanthoula Eirini Pantazi & Charalampos Paraskevas & Selçuk Arslan & Yücel Tekin & Bere Benjamin Bantchina & Yahya Ulusoy & Kemal Sulhi Gündoğdu & Muhammad Qaswar & Danyal Bust, 2024. "A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop," Agriculture, MDPI, vol. 14(2), pages 1-25, January.
    3. Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2021. "Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error," Energies, MDPI, vol. 14(24), pages 1-15, December.
    4. Mohamed K. Abdel-Fattah & Sameh Kotb Abd-Elmabod & Zhenhua Zhang & Abdel-Rhman M. A. Merwad, 2023. "Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions," Sustainability, MDPI, vol. 15(21), pages 1-15, October.
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