IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9392-d1168600.html
   My bibliography  Save this article

Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries

Author

Listed:
  • Hamzeh F. Assous

    (Finance Department, School of Business, King Faisal University, Al Ahsa 31982, Saudi Arabia)

  • Hazem AL-Najjar

    (Department of Computer, Abdul Aziz Al Ghurair School of Advanced Computing (ASAC), Luminus Technical University College, Amman 11732, Jordan)

  • Nadia Al-Rousan

    (MIS Department, Faculty of Business, Sohar University, Sohar 311, Oman)

  • Dania AL-Najjar

    (Finance Department, School of Business, King Faisal University, Al Ahsa 31982, Saudi Arabia)

Abstract

Crop yield prediction is one of the most challenging tasks in agriculture. It is considered to play an important role and be an essential step in decision-making processes. The goal of crop prediction is to establish food availability for the coming years, using different input variables associated with the crop yield domain. This paper aims to predict the yield of five of the Gulf countries’ crops: wheat, dates, watermelon, potatoes, and maize (corn). Five independent variables were used to develop a prediction model, namely year, rainfall, pesticide, temperature changes, and nitrogen (N) fertilizer; all these variables are calculated by year. Moreover, this research relied on one of the most widely used machine learning models in the field of crop yield prediction, which is the neural network model. The neural network model is used because it can predict complex relationships between independent and dependent variables. To evaluate the performance of the prediction models, different statistical evaluation metrics are adopted, including mean square error (MSE), root-mean-square error (RMSE), mean bias error (MBE), Pearson’s correlation coefficient, and the determination coefficient. The results showed that all Gulf countries are affected mainly by four independent variables: year, temperature changes, pesticides, and nitrogen (N) per year. Moreover, the average of the best crop yield prediction results for the Gulf countries showed that the RMSE and R 2 are 0.114 and 0.93, respectively. This provides initial evidence regarding the capability of the neural network model in crop yield prediction.

Suggested Citation

  • Hamzeh F. Assous & Hazem AL-Najjar & Nadia Al-Rousan & Dania AL-Najjar, 2023. "Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9392-:d:1168600
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9392/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9392/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. Jed Brown & Probir Das & Mohammad Al-Saidi, 2018. "Sustainable Agriculture in the Arabian/Persian Gulf Region Utilizing Marginal Water Resources: Making the Best of a Bad Situation," Sustainability, MDPI, vol. 10(5), pages 1-16, April.
    2. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
    3. Hasan Arda Burhan, 2022. "Crop Yield Prediction by Integrating Meteorological and Pesticides Use Data with Machine Learning Methods: An Application for Major Crops in Turkey," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(SI), pages 1-18.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tuba Tanyıldızı Ağır, 2024. "Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı," Sustainability, MDPI, vol. 16(9), pages 1-20, April.
    2. Patricio Vladimir Méndez-Zambrano & Luis Patricio Tierra Pérez & Rogelio Estalin Ureta Valdez & Ángel Patricio Flores Orozco, 2023. "Technological Innovations for Agricultural Production from an Environmental Perspective: A Review," Sustainability, MDPI, vol. 15(22), pages 1-15, November.
    3. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
    4. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    5. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    6. Shannak, Sa'd, 2022. "Optimizing dynamics of water-energy-food nexus in a desert climate," Energy Policy, Elsevier, vol. 164(C).
    7. Phon Sheng Hou & Lokman Mohd Fadzil & Selvakumar Manickam & Mahmood A. Al-Shareeda, 2023. "Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    8. Jawed Mustafa & Fahad Awjah Almehmadi & Saeed Alqaed & Mohsen Sharifpur, 2022. "Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    9. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    10. Abdulaziz I. Almulhim, 2024. "Toward a Greener Future: Applying Circular Economy Principles to Saudi Arabia’s Food Sector for Environmental Sustainability," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    11. Ağbulut, Ümit & Gürel, Ali Etem & Sarıdemir, Suat, 2021. "Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning alg," Energy, Elsevier, vol. 215(PA).
    12. Yan, Shicheng & Wu, Lifeng & Fan, Junliang & Zhang, Fucang & Zou, Yufeng & Wu, You, 2021. "A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China," Agricultural Water Management, Elsevier, vol. 244(C).
    13. Hasanov, Fakhri J. & Shannak, Sa'd, 2020. "Electricity incentives for agriculture in Saudi Arabia. Is that relevant to remove them?," Energy Policy, Elsevier, vol. 144(C).
    14. Elham Rahmani & Mohammad Khatami & Emma Stephens, 2024. "Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada," Sustainability, MDPI, vol. 16(5), pages 1-19, February.
    15. Jayashree T R & NV Subba Reddy & U Dinesh Acharya, 2023. "Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1013-1032, February.
    16. Amjad Aliewi & Jasim Al-Kandari & Asim Al-Khalid & Harish Bhandary & Habib Al-Qallaf, 2021. "Modelling the effect of high level of total dissolved solids (TDS) for the sustainable utilization of brackish groundwater from saline aquifers in Kuwait," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 2204-2223, February.
    17. Yamaç, Sevim Seda & Şeker, Cevdet & Negiş, Hamza, 2020. "Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area," Agricultural Water Management, Elsevier, vol. 234(C).
    18. Ahsan Ali & Yaseen A. Al-Mulla & Yassine Charabi & Ghazi Al-Rawas & Malik Al-Wardy, 2021. "Satellite-Based Water and Energy Balance Model for the Arid Region to Determine Evapotranspiration: Development and Application," Sustainability, MDPI, vol. 13(23), pages 1-22, November.
    19. Abdul Hameed & Sadiq Hussain & Aysha Rasheed & Muhammad Zaheer Ahmed & Sahar Abbas, 2024. "Exploring the Potentials of Halophytes in Addressing Climate Change-Related Issues: A Synthesis of Their Biological, Environmental, and Socioeconomic Aspects," World, MDPI, vol. 5(1), pages 1-22, January.
    20. Shahira A. Ahmed & Panagiotis Karanis, 2020. "Cryptosporidium and Cryptosporidiosis: The Perspective from the Gulf Countries," IJERPH, MDPI, vol. 17(18), pages 1-34, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9392-:d:1168600. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.