Affiliation:
1. Finance Department, School of Business, King Faisal University, Al Ahsa 31982, Saudi Arabia
2. Department of Computer, Abdul Aziz Al Ghurair School of Advanced Computing (ASAC), Luminus Technical University College, Amman 11732, Jordan
3. MIS Department, Faculty of Business, Sohar University, Sohar 311, Oman
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 R2 are 0.114 and 0.93, respectively. This provides initial evidence regarding the capability of the neural network model in crop yield prediction.
Funder
Ministry of Education of Saudi Arabia
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference37 articles.
1. A data-driven crop model for maize yield prediction;Chang;Commun. Biol.,2023
2. Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index;Assous;Complexity,2022
3. Al Najjar, D., Assous, H.F., Al-Najjar, H., and Al-Rousan, N. (J. Islam. Mark., 2022). Ramadan effect and indices movement estimation: A case study from eight Arab countries, J. Islam. Mark., ahead-of-print.
4. Bhimavarapu, U., Battineni, G., and Chintalapudi, N. (2023). Improved Optimization Algorithm in LSTM to Predict Crop Yield. Computers, 12.
5. Crop Yield Maximization Using an IoT-Based Smart Decision;Ikram;J. Sens.,2022