An accurate assessment of oil palm Fresh Fruit Bunch (FFB) ripeness is vital to maximize oil yield and ensure product quality. For a decade traditional method such as visual inspection and loose fruit counting remains common,due to simplicity but face limitations including subjectivity, labor shortage and low scalability. This review evaluates advances in sensor-based and artificial intelligence (AI) approaches for ripeness detection. The focus is on computer vision and deep learning models, multispectral and hyperspectral imaging as well as sensor-based systems. The integration of Internet of Things (IoT) and edge computing is also examined for real-time deployment. Based on the study, AI-driven methods, including Convolutional Neural Networks (CNNs) and YOLO frameworks, achieve accuracies above 95% in ripeness classification. Hyperspectral imaging combined with machine learning predicts oil content with more than 90% accuracy, while low-cost sensors demonstrate up to 94% accuracy in field conditions. IoT-enabled frameworks enhance scalability through continuous monitoring and localized decision-making. However, adoption is reminded limited due to high costs, computational demands, environmental variability and limited standardized datasets. Beyond technical constrains, socioeconomic barriers such as lack of expertise, inadequate infrastructure and affordability constrain adoption in resources limited context. As an assumption, a future research should prioritize on cost-effective sensors, adaptive algorithms, hybrid solutions and open-access datasets. It should also complement with supportive policies and farmer training to enhance accessibility to the new technology approach. The integration of artificial intelligence with imaging and sensor-based technologies constitutes a transformative approach for sustainable, scalable, and precise detection of FFB ripeness, thereby facilitating the advancement of the palm oil industry toward precision agriculture.
Hamid et al. (Wed,) studied this question.