Developing Feasible Intelligent Systems for Oil Palm Fresh Fruit Bunch Grading: A Review of Technological, Economic, and Social Dimensions
DOI:
https://doi.org/10.59890/mjst.v3i2.162Keywords:
Oil Palm, Fresh Fruit Bunch Grading, Intelligent Systems, Deep Learning, Computer Vision, Feasibility Analysis, Smallholders, Precision Agriculture, Sustainability, Technology AdoptionAbstract
The oil palm industry faces mounting pressure to improve operational efficiency while maintaining product quality amid labor shortages and sustainability imperatives. Traditional manual grading of fresh fruit bunches (FFB) is subject to inherent subjectivity, inconsistency, and scalability limitations, which directly affect oil extraction rates (OER) and economic returns. This qualitative literature review synthesizes contemporary evidence on intelligent system development for FFB grading, examining technological feasibility, economic viability, and socio-institutional dimensions from 2020 to 2025. The analysis integrates seventy-eight peer-reviewed studies to identify that computer vision systems based on RGB imagery and deep learning, particularly lightweight YOLO variants deployed on mobile edge devices, represent the most feasible solution across plantation scales. Hyperspectral imaging achieves superior accuracy (93–95%) but faces prohibitive costs and computational demands for widespread adoption. The review reveals that successful implementation hinges on contextual factors: plantation size, smallholder capacity, institutional support frameworks, and alignment with sustainability certification schemes. We find that technology adoption barriers among smallholders stem primarily from financial constraints, knowledge gaps, and weak institutional linkages rather than technological inadequacy
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