Developing Feasible Intelligent Systems for Oil Palm Fresh Fruit Bunch Grading: A Review of Technological, Economic, and Social Dimensions

The oil palm industry faces challenges in improving efficiency and consistency in quality, particularly in the assessment of fresh fruit bunches (FFB) maturity, which has been subjective. This article aims to identify the most viable intelligent system for FFB grading by reviewing technological, economic, and social aspects. Through a qualitative literature review of 78 studies (2020–2025), it was found that RGB-based computer vision systems and deep learning (particularly the YOLO variant on edge devices) are the most feasible due to their accuracy and relative affordability. Conversely, hyperspectral imaging is more precise but expensive and difficult to widely adopt. The success of implementation heavily depends on the scale of the plantation, the capacity of the farmers, and institutional support. These findings underscore the importance of a phased adoption model and supportive policies to ensure that digitalization of grading enhances productivity and sustainability in the palm oil sector.