Lightweight Deep Learning with Intra-Class Half-Mixing and Geometric Augmentation for Imbalanced Oil Palm Fresh Fruit Bunch Ripeness Classification

The precision of oil palm fresh fruit bunch (FFB) ripeness classification directly determines the extraction efficiency and chemical quality of the resulting crude palm oil (CPO). Traditional manual inspection at reception ramps remains labour-intensive, time-consuming, and subjective. To address these issues, this study presents a lightweight deep learning framework for automated oil palm FFB ripeness classification trained on a field-collected dataset of 857 images covering four ripeness classes (Over-ripe, Ripe, Under-ripe, and Unripe). Six lightweight classification backbones are evaluated, including MobileNetV2, EfficientNetV2B0/B1, and YOLO variants. An intra-class half-mixing augmentation with geometric transformation is proposed to address minority-class imbalance. Overall, YOLOv8n-cls achieved the highest accuracy (95.6%), followed by EfficientNetV2B0/B1, YOLO11n-cls, YOLO26n-cls, and MobileNetV2, respectively. In addition, all YOLO-family models achieved a recall score of 1.00 for the minority class while obtaining the highest F1 scores for the other classes. The experimental results suggest that the proposed augmentation method enables lightweight deep learning models to achieve promising classification performance on an imbalanced field-collected FFB dataset while improving minority-class detection.