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Literature Review Special Issue Palm Oil in Health and Technology
Exploring the role of generative artificial intelligence and large language models in enhancing the palm oil value chain: a systematic literature review
Loso Judijanto
Received: July 10, 2026 | Published: July 27, 2026
Citation: Judijanto L. Exploring the role of generative artificial intelligence and large language models in enhancing the palm oil value chain: a systematic literature review. Open Access J Sci. 2026;9(1):363-373. DOI: 10.15406/oajs.2026.09.00320
Abstract
The rapid advancement of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is transforming digital innovation in agribusiness by enabling intelligent automation, data-driven decision-making, and enhanced operational efficiency. However, their large-scale adoption in the palm oil value chain remains constrained by fragmented data, high administrative complexity, and infrastructural limitations. This study aims to systematically evaluate the current state of research on the applications, performance, and challenges of GenAI and LLMs across the palm oil value chain. A qualitative Systematic Literature Review (SLR) was conducted following the PRISMA framework. Publications were retrieved from the Scopus and PubMed databases. From an initial 1,083 records, 92 relevant studies were identified through refined search strategies. After applying inclusion criteria, including publication year (2020–2026), open-access availability, and duplicate removal, 35 peer-reviewed articles were selected for qualitative synthesis. The data were analyzed using thematic coding and cross-study synthesis to identify dominant application areas, technical architectures, and implementation trends. The review identifies four major application domains: predictive maintenance and agricultural robotics, sustainability documentation and compliance, precision agronomy decision support, and supply chain logistics optimization. Transformer-based architectures and Retrieval-Augmented Generation (RAG) emerged as the dominant technologies, with most studies focusing on Indonesia and Malaysia. The reviewed evidence indicates that GenAI and LLMs improve harvesting accuracy, optimize maintenance and logistics, enhance precision agronomy, and automate sustainability compliance while strengthening traceability across the value chain. Nevertheless, adoption remains constrained by data scarcity, computational costs, limited digital infrastructure, and the smallholder digital divide. Future research should prioritize domain-specific foundation models, edge-AI solutions, and inclusive deployment strategies to support sustainable and scalable implementation across the palm oil industry.
Keywords: generative AI, large language models, palm oil, value chain, systematic literature review
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