When and Where Can Automated Systems for Oil Palm Fresh Fruit Bunch Ripeness Detection Be Effectively Implemented? A Review

This research examines the implementation of an automated system for detecting the ripeness of Fresh Fruit Bunches (FFB) using artificial intelligence and computer vision technology. The main objective of the study is to identify when and where the automated ripeness detection system can be effectively applied in the oil palm industry. Using a Systematic Literature Review (SLR) approach, this research analyzes 34 peer-reviewed articles published between 2020 and 2026. The study results indicate that this system is most effectively implemented during a labor crisis (shortage of 12-15%), the digital transformation phase of the supply chain, and when processing delays exceed 24 hours for more than 30% of harvested FFB. These findings suggest a phased implementation, starting from grading stations at the mill and accompanied by infrastructure investment in plantation areas. This research provides policy recommendations leading to the adoption of inclusive and sustainable technology.