Omar Zaid, P P Abdul Majeed Anwar, Rosbi Munirah, Ghazalli Shuwaibatul Aslamiah, Selamat Hazlina
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia.
School of Engineering and Technology, Sunway University, 47500 Selangor, Malaysia.
Data Brief. 2024 Jun 26;55:110667. doi: 10.1016/j.dib.2024.110667. eCollection 2024 Aug.
This dataset comprises oil palm fresh fruit bunch (FFB) images that may potentially be used in the study related to fruit ripeness detection via image processing. The FFB dataset was collected from palm oil plantations in Johor, Negeri Sembilan, and Perak, Malaysia. The data collection involved acquiring pictures of FFB from various angles and classifying them based on their ripeness level, categorised into five classes: damaged bunch, empty bunch, unripe, ripe, and overripe. An experienced grader carefully labelled each FFB image with the corresponding ground truth information. The dataset provides valuable insights into the colour variations of FFBs throughout their ripening process, which is essential for assessing oil quality. It includes observations on the external fruit colours as well as characteristics related to the presence of empty sockets in the FFB as a key indicator of ripeness. The reusability potential of this dataset is significant for researchers in the field of oil palm fruit classification and grading, which requires an extensive outdoor dataset that comprise FFB's both on the tree and on the ground. Our work enables the development and validation of machine learning pipelines for outdoor automated FFB grading. Furthermore, the dataset may also support studies to improve oil palm cultivation practices, enhance yield, and optimise oil quality.
该数据集包含油棕新鲜果串（FFB）图像，这些图像可能用于通过图像处理进行果实成熟度检测的研究。FFB数据集是从马来西亚柔佛州、森美兰州和霹雳州的棕榈油种植园收集的。数据收集包括从各个角度获取FFB的图片，并根据其成熟度水平进行分类，分为五类：受损果串、空果串、未成熟、成熟和过熟。一位经验丰富的分级员仔细地为每个FFB图像标注了相应的真实信息。该数据集提供了关于FFB在整个成熟过程中颜色变化的有价值见解，这对于评估油的质量至关重要。它包括对外部果实颜色的观察以及与FFB中空果柄的存在相关的特征，空果柄是成熟度的关键指标。该数据集的可重用潜力对于油棕果实分类和分级领域的研究人员来说非常重要，该领域需要一个广泛的户外数据集，包括树上和地面上的FFB。我们的工作能够开发和验证用于户外自动FFB分级的机器学习管道。此外，该数据集还可能支持旨在改善油棕种植实践、提高产量和优化油质量的研究。
