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

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.

Influence of Membership Function and Degree on Sorghum Growth Prediction Models in Machine Learning

“…Recent research on crop yield predictions utilizing machine learning techniques has emphasized the incorporation of several key input parameters. Rainfall has been identified as a significant factor in crop yield prediction, with multiple studies exploring its impact [16] [17] [18] [19], Temperature, humidity, and climate have also emerged as primary concerns, with some studies employing multiple linear regression to analyze weather forecasts by considering these parameters [16] [20] [18] [19] [21]. Moreover, soil pH and irrigation, as determinants of soil quality and optimal irrigation, are integral www.ijacsa.thesai.org components of crop yield prediction models [19] [22] [21].…”

“…Wind speed, an external factor affecting plant growth, is considered in several research endeavors [20] [21]. Additionally, the location of crops is recognized as a crucial parameter in crop yield prediction, with crop location data serving as a variable in predictive analyses [18] [23]. These recent studies combine machine learning technology with a deep understanding of these diverse factors to enhance the accuracy of crop yield predictions.…”