A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop
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 sc...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/14/2/210 |
| _version_ | 1797299237102288896 |
|---|---|
| author | Konstantinos Dolaptsis Xanthoula Eirini Pantazi Charalampos Paraskevas Selçuk Arslan Yücel Tekin Bere Benjamin Bantchina Yahya Ulusoy Kemal Sulhi Gündoğdu Muhammad Qaswar Danyal Bustan Abdul Mounem Mouazen |
| author_facet | Konstantinos Dolaptsis Xanthoula Eirini Pantazi Charalampos Paraskevas Selçuk Arslan Yücel Tekin Bere Benjamin Bantchina Yahya Ulusoy Kemal Sulhi Gündoğdu Muhammad Qaswar Danyal Bustan Abdul Mounem Mouazen |
| author_sort | Konstantinos Dolaptsis |
| collection | DOAJ |
| description | 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<sup>2</sup> 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. |
| first_indexed | 2024-03-07T22:46:47Z |
| format | Article |
| id | doaj.art-a108d7804ee74e1fb7a77f854b12e675 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-07T22:46:47Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-a108d7804ee74e1fb7a77f854b12e6752024-02-23T15:03:36ZengMDPI AGAgriculture2077-04722024-01-0114221010.3390/agriculture14020210A Hybrid LSTM Approach for Irrigation Scheduling in Maize CropKonstantinos Dolaptsis0Xanthoula Eirini Pantazi1Charalampos Paraskevas2Selçuk Arslan3Yücel Tekin4Bere Benjamin Bantchina5Yahya Ulusoy6Kemal Sulhi Gündoğdu7Muhammad Qaswar8Danyal Bustan9Abdul Mounem Mouazen10Laboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceLaboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceLaboratory of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Biosystems Engineering, Faculty of Agriculture, Bursa Uludag University, 16059 Bursa, TurkeyVocational School of Technical Sciences, Bursa Uludag University, 16059 Bursa, TurkeyDepartment of Biosystems Engineering, Natural and Applied Sciences Institute, Bursa Uludag University, 16059 Bursa, TurkeyVocational School of Technical Sciences, Bursa Uludag University, 16059 Bursa, TurkeyDepartment of Biosystems Engineering, Faculty of Agriculture, Bursa Uludag University, 16059 Bursa, TurkeyDepartment of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Gent, BelgiumDepartment of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Gent, BelgiumDepartment of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Gent, BelgiumIrrigation 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<sup>2</sup> 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.https://www.mdpi.com/2077-0472/14/2/210precision agricultureartificial intelligencelong short-term memorypredictive controldeep learningmoisture content |
| spellingShingle | Konstantinos Dolaptsis Xanthoula Eirini Pantazi Charalampos Paraskevas Selçuk Arslan Yücel Tekin Bere Benjamin Bantchina Yahya Ulusoy Kemal Sulhi Gündoğdu Muhammad Qaswar Danyal Bustan Abdul Mounem Mouazen A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop Agriculture precision agriculture artificial intelligence long short-term memory predictive control deep learning moisture content |
| title | A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop |
| title_full | A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop |
| title_fullStr | A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop |
| title_full_unstemmed | A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop |
| title_short | A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop |
| title_sort | hybrid lstm approach for irrigation scheduling in maize crop |
| topic | precision agriculture artificial intelligence long short-term memory predictive control deep learning moisture content |
| url | https://www.mdpi.com/2077-0472/14/2/210 |
| work_keys_str_mv | AT konstantinosdolaptsis ahybridlstmapproachforirrigationschedulinginmaizecrop AT xanthoulaeirinipantazi ahybridlstmapproachforirrigationschedulinginmaizecrop AT charalamposparaskevas ahybridlstmapproachforirrigationschedulinginmaizecrop AT selcukarslan ahybridlstmapproachforirrigationschedulinginmaizecrop AT yuceltekin ahybridlstmapproachforirrigationschedulinginmaizecrop AT berebenjaminbantchina ahybridlstmapproachforirrigationschedulinginmaizecrop AT yahyaulusoy ahybridlstmapproachforirrigationschedulinginmaizecrop AT kemalsulhigundogdu ahybridlstmapproachforirrigationschedulinginmaizecrop AT muhammadqaswar ahybridlstmapproachforirrigationschedulinginmaizecrop AT danyalbustan ahybridlstmapproachforirrigationschedulinginmaizecrop AT abdulmounemmouazen ahybridlstmapproachforirrigationschedulinginmaizecrop AT konstantinosdolaptsis hybridlstmapproachforirrigationschedulinginmaizecrop AT xanthoulaeirinipantazi hybridlstmapproachforirrigationschedulinginmaizecrop AT charalamposparaskevas hybridlstmapproachforirrigationschedulinginmaizecrop AT selcukarslan hybridlstmapproachforirrigationschedulinginmaizecrop AT yuceltekin hybridlstmapproachforirrigationschedulinginmaizecrop AT berebenjaminbantchina hybridlstmapproachforirrigationschedulinginmaizecrop AT yahyaulusoy hybridlstmapproachforirrigationschedulinginmaizecrop AT kemalsulhigundogdu hybridlstmapproachforirrigationschedulinginmaizecrop AT muhammadqaswar hybridlstmapproachforirrigationschedulinginmaizecrop AT danyalbustan hybridlstmapproachforirrigationschedulinginmaizecrop AT abdulmounemmouazen hybridlstmapproachforirrigationschedulinginmaizecrop |