An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading

This study hypothesizes that an ordinal dataset tailored for ripeness classification can enhance automated quality grading in the palm oil industry. The dataset, comprising 4,728 high-resolution images of oil palm fruits categorized into five ripeness levels (Immature, Partially Ripe, Fully Ripe, Overripe, and Decayed), was collected in real-world conditions from Central Kalimantan, Indonesia, using diverse devices and capturing environmental variability such as lighting, poses, and natural backgrounds. Defined with expert input, the categories reflect biological and economic relevance, addressing challenges like imbalanced data distributions typical in agriculture. Notable findings include the dataset’s authenticity, which mirrors real-world agricultural conditions and enables the development of robust machine learning models. With stratified splits for training, validation, and testing, the dataset facilitates benchmarking while supporting techniques like weighted loss functions to address imbalances. Its diversity and realistic complexity make it a valuable resource for advancing ordinal regression and automated grading systems, driving efficiency and sustainability in palm oil production.