Oil Palm Ripening Detection Technologies: A Review of Contemporary AI Techniques and Multi-Criteria Effectiveness Evaluation Frameworks

This research examines the development of oil palm fruit ripeness detection technology, which plays a crucial role in determining the optimal harvest time to enhance oil extraction rates and the quality of crude palm oil. The study aims to map various ripeness detection techniques, evaluate their effectiveness, and formulate a comprehensive technology assessment framework. The method used is a qualitative literature review of 88 scientific publications from the period 2020–2026 with a thematic synthesis approach. The study results indicate that artificial intelligence-based technology—particularly deep learning such as CNN and YOLO—has become the most dominant approach with accuracy above 95%, while spectroscopy methods offer very high precision but are limited by cost and implementation. The integration of IoT sensors, drones, and remote sensing also opens opportunities for integrated plantation monitoring. This research emphasizes that the selection of technology must consider technical, economic, and accessibility aspects, and requires policy support, cooperative models, and farmer capacity building to ensure that precision agriculture innovations can be inclusively and sustainably adopted in the global palm oil industry.