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A hybrid LSTM approach for irrigation scheduling in maize crop

Konstantinos Dolaptsis, Xanthoula Eirini Pantazi, Charalampos Paraskevas, Selcuk Arslan, Yucel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, Kemal Sulhi Guendogdu, Muhammad Qaswar (UGent) , Danyal Bustan (UGent) , et al.
(2024) AGRICULTURE-BASEL. 14(2).
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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 R2 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, SOIL-WATER CONTENT, MODEL

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MLA
Dolaptsis, Konstantinos, et al. “A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop.” AGRICULTURE-BASEL, vol. 14, no. 2, 2024, doi:10.3390/agriculture14020210.
APA
Dolaptsis, K., Pantazi, X. E., Paraskevas, C., Arslan, S., Tekin, Y., Bantchina, B. B., … Mouazen, A. (2024). A hybrid LSTM approach for irrigation scheduling in maize crop. AGRICULTURE-BASEL, 14(2). https://doi.org/10.3390/agriculture14020210
Chicago author-date
Dolaptsis, Konstantinos, Xanthoula Eirini Pantazi, Charalampos Paraskevas, Selcuk Arslan, Yucel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, et al. 2024. “A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop.” AGRICULTURE-BASEL 14 (2). https://doi.org/10.3390/agriculture14020210.
Chicago author-date (all authors)
Dolaptsis, Konstantinos, Xanthoula Eirini Pantazi, Charalampos Paraskevas, Selcuk Arslan, Yucel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, Kemal Sulhi Guendogdu, Muhammad Qaswar, Danyal Bustan, and Abdul Mouazen. 2024. “A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop.” AGRICULTURE-BASEL 14 (2). doi:10.3390/agriculture14020210.
Vancouver
1.
Dolaptsis K, Pantazi XE, Paraskevas C, Arslan S, Tekin Y, Bantchina BB, et al. A hybrid LSTM approach for irrigation scheduling in maize crop. AGRICULTURE-BASEL. 2024;14(2).
IEEE
[1]
K. Dolaptsis et al., “A hybrid LSTM approach for irrigation scheduling in maize crop,” AGRICULTURE-BASEL, vol. 14, no. 2, 2024.
@article{01HT56905PXVWT5A8EAHJ7R46V,
  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 R2 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.}},
  articleno    = {{210}},
  author       = {{Dolaptsis, Konstantinos and  Pantazi, Xanthoula Eirini and  Paraskevas, Charalampos and  Arslan, Selcuk and  Tekin, Yucel and  Bantchina, Bere Benjamin and  Ulusoy, Yahya and  Guendogdu, Kemal Sulhi and Qaswar, Muhammad and Bustan, Danyal and Mouazen, Abdul}},
  issn         = {{2077-0472}},
  journal      = {{AGRICULTURE-BASEL}},
  keywords     = {{precision agriculture,artificial intelligence,long short-term memory,predictive control,deep learning,moisture content,water management,time series analysis,SOIL-WATER CONTENT,MODEL}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{25}},
  title        = {{A hybrid LSTM approach for irrigation scheduling in maize crop}},
  url          = {{http://doi.org/10.3390/agriculture14020210}},
  volume       = {{14}},
  year         = {{2024}},
}
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