AI-Driven Quality Control and Ripeness Classification

This research is motivated by the importance of quality control in the palm oil industry, particularly in the classification of fresh fruit bunches (FFB) maturity, which affects processing efficiency. The study aims to systematically review the application of artificial intelligence (AI), specifically computer vision and deep learning, in the classification of FFB maturity. The method used is a Systematic Literature Review (SLR) that analyzes 37 scientific articles (2020–2026). The research results indicate that deep learning technology, especially convolutional neural networks (CNN), can achieve accuracy of over 90% in classifying FFB maturity, with significant improvements using transfer learning techniques. The implications of this research suggest that the application of AI in FFB quality control can enhance consistency and operational efficiency, and has great potential to be integrated into palm oil processing systems.