Data-driven rice yield predictions and prescriptive analytics for sustainable agriculture in Malaysia

• Main Authors: Marong, Muhammad, Husin, Nor Azura, Zolkepli, Maslina, Affendey, Lilly Suriani
• Format: Article
• Language: English
• Published: Science and Information Organization 2024
• Online Access: http://psasir.upm.edu.my/id/eprint/112884/1/112884.pdf; http://psasir.upm.edu.my/id/eprint/112884/; https://thesai.org/Publications/ViewPaper?Volume=15&Issue=3&Code=IJACSA&SerialNo=37

Maximizing rice yield is critical for ensuring food security and sustainable agriculture in Malaysia. This research investigates the impact of environmental conditions and management methods on crop yields, focusing on accurate predictions to inform decision-making by farmers. Utilizing machine learning algorithms as decision-support tools, the study analyses commonly used models—Linear Regression, Support Vector Machines, Random Forest, and Artificial Neural Networks—alongside key environmental factors such as temperature, rainfall, and historical yield data. A comprehensive dataset for rice yield prediction in Malaysia was constructed, encompassing yield data from 2014 to 2018. To elucidate the influence of climatic factors, long-term rainfall records spanning 1981 to 2018 were incorporated into the analysis. This extensive dataset facilitates the exploration of recent agricultural trends in Malaysia and their relationship to rice yield. The study specifically evaluates the performance of Random Forest, Support Vector Machine (SVM), and Neural Network (NN) models using metrics like Correlation Coefficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). Results reveal Random Forest as the standout performer with a Correlation Coefficient of 0.954, indicating a robust positive linear relationship between predictions and actual yield data. SVM and NN also exhibit respectable Correlation Coefficients of 0.767 and 0.791, respectively, making them effective tools for rice yield prediction in Malaysia. By integrating diverse environmental and management factors, the proposed methodology enhances prediction accuracy, enabling farmers to optimize practices for better economic outcomes. This approach holds significant potential for contributing to sustainable agriculture, improved food security, and enhanced economic efficiency in Malaysia's rice farming sector. Leveraging machine learning, the research aims to transform rice yield prediction into a proactive decision-making tool, fostering a resilient and productive agrarian ecosystem in Malaysia. © (2024), (Science and Information Organization). All Rights Reserved.