Assessment of rice yield prediction models based on big data analytics for better supply chain decision-making in Thailand
by Sumanya Ngandee; Attaphongse Taparugssanagorn; Chutiporn Anutariya; John K.M. Kuwornu
International Journal of Value Chain Management (IJVCM), Vol. 12, No. 3, 2021

Abstract: This study examined rice yield prediction models for the main type of in-season rice cultivated in Thailand. Models were generated using the machine learning (ML) algorithms: generalised linear model (GLM), feed-forward neural network (FFNN), support vector machine (SVM), and random forest (RF). The models were evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), and R2 statistic. The results show that the FFNN, which is a deep neural network outperforms the other models. In addition, the FFNN can simultaneously account for complex nonlinear relationships in high-dimensional datasets. While the Big-O complexity and the execution runtime of the FFNN exceed the other models, its execution of predictions takes the least execution runtime. The practical implication of this study is to improve the quality of agricultural information dissemination services and the general public for the development of Thailand's agricultural sector, rice supply chains and the economy as a whole.

Online publication date: Tue, 19-Oct-2021

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