Impact of economic indicators on rice production: A machine learning approach in Sri Lanka

• Published in: PloS one 2024-06, Vol.19 (6), p.e0303883
• Main Authors: Kularathne, Sherin, Rathnayake, Namal, Herath, Madhawa, Rathnayake, Upaka, Hoshino, Yukinobu
• Format: Article
• Language: English
• Subjects: Agricultural land; Agricultural production; Agriculture; Agriculture - economics; Algorithms; Analysis; Arable land; Biology and Life Sciences; Climate change; Computer and Information Sciences; Consumption; Crop production; Crop Production - economics; Crops, Agricultural - economics; Crops, Agricultural - growth & development; Data collection; Data entry; Decision making; Defense spending; Economic aspects; Economic factors; Economic forecasting; Economic growth; Economic indicators; Ensemble learning; Food; Fuzzy logic; Gaussian process; GDP; Grain industry; Gross Domestic Product; Imports; Indicators; Inflation (Finance); International economic relations; Learning algorithms; Machine Learning; Macroeconomics; Neural networks; Oryza - growth & development; People and places; Physical Sciences; Population growth; Precipitation; Prediction models; Production data; Regression analysis; Research and Analysis Methods; Rice; Securities markets; Social Sciences; Sri Lanka; Support Vector Machine; Support vector machines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0303883

Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models using machine learning techniques. The dataset, spanning from 1960 to 2020, includes key economic variables such as GDP, inflation rate, manufacturing output, population, population growth rate, imports, arable land area, military expenditure, and rice production. The study's findings reveal the significant influence of economic factors on rice production in Sri Lanka. Machine learning models, including Linear Regression, Support Vector Machines, Ensemble methods, and Gaussian Process Regression, demonstrate strong predictive accuracy in forecasting rice production based on economic indicators. These results underscore the importance of economic indicators in shaping rice production outcomes and highlight the potential of machine learning in predicting agricultural trends. The study suggests avenues for future research, such as exploring regional variations and refining models based on ongoing data collection.