Machine Learning to Predict Rice Stock for Food Security: A Brief Study - Herawati, Is Eka; ., Marhamah; Asnamawati, Lina; ., Mukhtar (International Journal of Membrane Science and Technology, 2023)
The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” and “rice”. Only journal papers were considered eligible that were published within 2020 – 2023. Approximately 49 articles from scopus and google scholar database. It was selected for full content review after the pre-screening process. The results indicated that this topic pertains to different disciplines that favor convergence research at the international level. Prediction criteria related to food security were found to be the most inputs of prediction models. Machine learning models such as Random forest result in high accuracy in predict rice stock on 98%. This study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in predict rice stock and contributing to a more systematic research on this topic.