AI in Palm Oil Yield Production, Disease Detection, and Process Optimization: A Systematic Review

This journal discusses the utilization of artificial intelligence or AI in the oil palm industry to address challenges in productivity enhancement, disease detection, process efficiency, and sustainability. The study aims to map AI research on yield prediction, disease detection, and operational optimization, while also assessing the performance and limitations of its methods. Using a PRISMA-based Systematic Literature Review approach, the research examines 35 Scopus studies from the period 2022–2025. Findings indicate that models such as CNN, Random Forest, and hybrid approaches can achieve high accuracy in yield prediction, disease detection, and process efficiency, especially when combined with remote sensing and IoT. However, its application is still constrained by data quality, variations in plantation conditions, cross-system integration, and model interpretability. The implication is that AI has the potential to strengthen more precise, efficient, and sustainable oil palm plantation management.