Enhancing Oil Palm Productivity through Strategic Implementation of Fresh Fruit Bunch Grading using Artificial Intelligence Technology: A Review

This research discusses the importance of increasing the productivity of the oil palm industry through the implementation of a fresh fruit bunch (FFB) quality assessment system. The background is the still low oil extraction rate due to subjective and inconsistent manual grading practices. This study aims to analyze the effectiveness of FFB grading implementation, particularly through artificial intelligence technology, in improving productivity and production quality. The method used is a qualitative literature review of scientific publications and technical reports from the period 2020–2026 with a thematic analysis approach. The study results show that an automated grading system based on computer vision and deep learning can increase the oil extraction rate by approximately 8–12% with a classification accuracy of more than 95%. However, the adoption of technology still faces barriers in terms of cost, technical capacity, and access for smallholders. This research emphasizes the need for policy support, strengthening farmer capacity, as well as technological and market incentives to accelerate the implementation of a grading system that supports productivity and sustainability in the palm oil sector.