A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop

Konstantinos Dolaptsis, Xanthoula Eirini Pantazi (), Charalampos Paraskevas, Selçuk Arslan, Yücel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, Kemal Sulhi Gündoğdu, Muhammad Qaswar, Danyal Bustan and Abdul Mounem Mouazen
Additional contact information
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
Abdul Mounem Mouazen: Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium

Agriculture, 2024, vol. 14, issue 2, 1-25

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.

Keywords: precision agriculture; artificial intelligence; long short-term memory; predictive control; deep learning; moisture content; water management; time series analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/2/210/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/2/210/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:2:p:210-:d:1328191

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().