Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia.
Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia.
Sci Data. 2023 Feb 4;10(1):72. doi: 10.1038/s41597-023-01958-x.
The quality of palm oil is strongly influenced by the maturity level of the fruit to be processed into palm oil. Many studies have been carried out for detecting and classifying the maturity level of oil palm fruit to improve the quality with the use of computer vision. However, most of these studies use datasets in the form of images of oil palm fresh fruit bunches (FFB) with incomplete categorization according to real conditions in palm oil mills. Therefore, this study introduces a new complete dataset obtained directly from palm oil mills in the form of videos and images with different categories in accordance with the real conditions faced by the grading section of the palm oil mill. The video dataset consists of 45 videos with a single category of FFB videos and 56 videos with a collection of FFB with multiple categories for each video. Videos are collected using a smart phone with a size of 1280 × 720 pixels with .mp4 format. In addition, this dataset has also been annotated and labelled based on the maturity level of oil palm fruit with 6 categories, which are unripe, under-ripe, ripe, overripe, empty bunches and abnormal fruit.
棕榈油的质量受到待加工成棕榈油的果实成熟度的强烈影响。为了提高油棕果实的质量，许多研究都使用计算机视觉来检测和分类油棕果实的成熟度。然而，这些研究大多使用的是不完全按照棕榈油厂实际情况进行分类的新鲜油棕果串（FFB）图像数据集。因此，本研究介绍了一种新的完整数据集，该数据集直接来自棕榈油厂，以视频和图像的形式呈现，并且根据棕榈油厂分级部分面临的实际情况进行了不同类别的划分。视频数据集由 45 个单一 FFB 视频类别视频和 56 个多个类别 FFB 视频组成。视频使用 1280×720 像素大小的智能手机以.mp4 格式进行采集。此外，该数据集还根据油棕果实的成熟度进行了标注和标记，分为未成熟、未完全成熟、成熟、过熟、空果串和异常果实 6 个类别。
