Improved information dissemination services for the agricultural sector in Thailand: development and evaluation of a machine learning based rice crop yield prediction system

Abstract

The upward surge in world population, sustainability, and concerns about food and nutritional security place the food and agricultural sector amidst challenges. These challenges encompass increasing food supply, meeting higher quality standards, and enhancing productivity and crop yield prediction. This study discusses the development and evaluation of a Machine Learning (ML) based rice yield prediction system. Utilizing extensive historical datasets from the Office of Agricultural Economics (OAE), Ministry of Agriculture and Cooperatives (MOAC), Thai Meteorological Department, and Department of Internal Trade of Thailand, all pivotal variables at the national rice level were considered. Examining ML models like the Generalized Linear Model (GLM), Feed-Forward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest (RF), the study proposes a Web-based system to disseminate Thailand's rice information and yield predictions, aiding decision-making processes. Beyond evaluating the performance of each prediction model, user satisfaction was scrutinized. Results reveal that the FFNN, a deep neural network, adeptly handles complex nonlinear relationships in high-dimensional datasets. Despite the FFNN's longer training runtime due to Big-O complexity, it exhibits the shortest execution time for predictions. System usability assessment, based on ten standardized questions, indicates participants found the proposed system marginally acceptable, reporting positive user experiences and feeling confident in system use without perceiving it as inconsistent or cumbersome. This study is significant for enhancing agricultural information dissemination services across diverse sectors, benefiting farmers, wholesalers, retailers, and policymakers.