Naftali Martinus Grady, Hugo Gregory, Priyadi Muhammad Reza Azhar, Asrol Muhammad, Utama Ditdit Nugeraha
Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia.
Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, Jakarta, 10480, Indonesia.
Sci Data. 2025 Jun 10;12(1):972. doi: 10.1038/s41597-025-05227-x.
This article presents a dataset of oil palm Fresh Fruit Bunches (FFBs) images from commercial plantations in Central Kalimantan, Indonesia, focusing on five maturity stages: Unripe, Underripe, Ripe, Flower, and Abnormal. The data collection involved smartphone video recordings of unharvested trees from multiple angles under varying conditions. Video frames were extracted and expertly annotated using Computer Vision Annotation Tool (CVAT), with annotations exported in Common Objects in Context (COCO) format suitable for object detection tasks. It has 10,207 images in its training set, 2,896 in the validation set, and 1,400 in the test set, which are supplemented using data augmentation to handle class imbalance and increase variation. These images have real-world complications arising from partial visibility, low contrast, occlusion, and blurriness. It forms the basis that will support the development of deep learning models for detection and classification of FFB, particularly for monitoring of harvest times, yield prediction, and optimization of resources in plantation operations.
