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Literature Review Special Issue Palm Oil in Health and Technology
Artificial intelligence applications in sustainable palm oil production and marketing: a systematic literature review
Loso Judijanto
Received: March 30, 2026 | Published: May 5, 2026
Citation: Judijanto L. Artificial intelligence applications in sustainable palm oil production and marketing: a systematic literature review. Open Access J Sci. 2026;9(1):174-185. DOI: 10.15406/oajs.2026.09.00302
Abstract
As digital transformation advances across agricultural commodity systems, Artificial Intelligence (AI) has gained increasing attention as a strategic enabler of efficiency, transparency, and sustainability within palm oil production and marketing. Although scholarly contributions on AI in this sector have expanded in recent years, existing evidence remains dispersed across technical domains and lacks a comprehensive synthesis. This study systematically identifies and integrates peer-reviewed research examining AI-driven applications that support sustainability within the palm oil ecosystem. The research adopts a Systematic Literature Review (SLR) design supported by a transparent and replicable screening protocol. Relevant scientific papers were sourced entirely from Scopus using a systematic keyword-based search strategy. An initial publication-period filter (2020–2026) and open-access screening yielded a final corpus of 31 peer-reviewed articles. The analysis employed thematic synthesis and methodological mapping to classify technological approaches, application domains, and reported performance outcomes. Specifically, the review followed PRISMA-guided identification, screening, eligibility, and inclusion stages, and each article was coded according to AI technique, application domain, dataset characteristics, validation approach, and reported performance indicators such as accuracy, precision, recall, F1-score, RMSE, MAPE, and operational efficiency gains. The findings reveal five dominant clusters: precision agriculture and yield optimization, crop health monitoring, environmental performance assessment, supply chain traceability, and market intelligence. Across these domains, machine learning and deep learning models demonstrate improvements in predictive accuracy, resource optimization, monitoring reliability, logistics coordination, and market forecasting stability. The review concludes that AI serves as an enabling analytical infrastructure that supports productivity enhancement and sustainability governance within palm oil systems. Future research should prioritize integrated valuechain modeling, longitudinal validation, and scalable interdisciplinary frameworks.
Keywords: artificial intelligence, sustainable palm oil, precision agriculture, supply chain traceability, market forecasting
©2026 Judijanto. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.