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A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop

Author

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  • Konstantinos Dolaptsis

    (Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Xanthoula Eirini Pantazi

    (Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Charalampos Paraskevas

    (Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Selçuk Arslan

    (Department of Biosystems Engineering, Faculty of Agriculture, Bursa Uludag University, 16059 Bursa, Turkey)

  • Yücel Tekin

    (Vocational School of Technical Sciences, Bursa Uludag University, 16059 Bursa, Turkey)

  • Bere Benjamin Bantchina

    (Department of Biosystems Engineering, Natural and Applied Sciences Institute, Bursa Uludag University, 16059 Bursa, Turkey)

  • Yahya Ulusoy

    (Vocational School of Technical Sciences, Bursa Uludag University, 16059 Bursa, Turkey)

  • Kemal Sulhi Gündoğdu

    (Department of Biosystems Engineering, Faculty of Agriculture, Bursa Uludag University, 16059 Bursa, Turkey)

  • Muhammad Qaswar

    (Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium)

  • Danyal Bustan

    (Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium
    Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Quchan University of Technology, Quchan 94771-67335, Iran)

  • Abdul Mounem Mouazen

    (Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium)

Abstract

Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation scheduling in maize fields in Bursa, Turkey. A critical aspect of the study was the use of the Aquacrop 7.0 model to simulate soil moisture content (MC) data due to data limitations in the investigated fields. This simulation model, developed by the Food and Agriculture Organization (FAO), helped overcome gaps in soil sensor data, enhancing the LSTM model’s predictions. The LSTM model was trained and tuned using a combination of soil, weather, and satellite-based plant vegetation data in order to predict soil moisture content (MC) reductions. The study’s results indicated that the LSTM model, supported by Aquacrop 7.0 simulations, was effective in predicting MC reduction across various time phases of the maize growing season, attaining R 2 values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2, demonstrating the potential of this approach for precise and efficient agricultural irrigation practices.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:2:p:210-:d:1328191
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    References listed on IDEAS

    as
    1. Feng Wang & Jun Xue & Ruizhi Xie & Bo Ming & Keru Wang & Peng Hou & Lizhen Zhang & Shaokun Li, 2022. "Assessing Growth and Water Productivity for Drip-Irrigated Maize under High Plant Density in Arid to Semi-Humid Climates," Agriculture, MDPI, vol. 12(1), pages 1-16, January.
    2. Sandhu, Rupinder & Irmak, Suat, 2019. "Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    3. Kanwalpreet Kour & Deepali Gupta & Junaid Rashid & Kamali Gupta & Jungeun Kim & Keejun Han & Khalid Mohiuddin, 2023. "Smart Framework for Quality Check and Determination of Adulterants in Saffron Using Sensors and AquaCrop," Agriculture, MDPI, vol. 13(4), pages 1-21, March.
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