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

Hamzeh F. Assous, Hazem AL-Najjar, Nadia Al-Rousan and Dania AL-Najjar ()
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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

Sustainability, 2023, vol. 15, issue 12, 1-21

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.

Keywords: crop yield prediction; food security; neural network; gulf countries; Pearson’s correlation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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