HyperspectralBlueberries: a dataset of hyperspectral reflectance images of normal and defective blueberries

The HyperspectralBluberries dataset consists of hyperspectral datacubes, which were acquired by an in-house assembled benchtop line scanning system, from 420 blueberries of two categories, including 210 sound fruit and 210 samples with various defects. The fruit samples were hand-picked from a commercial orchard. Each scanning event, which was done for an array of 42 samples, yields two files in image formats .bil (band-interleaved-by-line) and .hdr (header), which store the hyperspectral raw data and associated metadata, respectively, and are both necessary for loading hyperspectral data for processing.  In addition to sample scanning, a white reference was also scanned, which can be used for standardizing spectral responses. As a result, there are 22 files in the dataset, totaling about 25 GB in file size. The sample file names are descriptive, indicating the blueberry category and number information. The dataset was used for developing machine learning models for differentiating between normal and defective blueberries, achieving an overall accuracy of 96.6%. Software programs for the modeling work are publicly available at: https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging.

Details about the dataset curation and modeling experiments are described in the journal article: Deng, B., Lu, Y., Stafne, E. (2024). Fusing Spectral and Spatial Features of Hyperspectral Reflectance Imagery for Differentiating between Normal and Defective Blueberries. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2024.100473. If you use the dataset in published research, please consider citing the dataset or the journal article. Hopefully, you find the dataset useful.

本数据集为高光谱蓝莓数据集（HyperspectralBlueberries），其包含由自研组装的台式线扫描系统采集的高光谱数据立方体，样本取自两类共420颗蓝莓：210颗健康果实与210颗带有各类缺陷的样本。所有果实样本均采自商业果园，采用人工采摘方式。单次扫描针对42个样本阵列进行，会生成两个图像格式文件：.bil（波段按行交叉格式，band-interleaved-by-line）与.hdr（头文件，header），二者分别存储高光谱原始数据与关联元数据，均为加载并处理高光谱数据的必需文件。除样本扫描外，数据集还包含白色参考扫描数据，可用于标准化光谱响应。综上，本数据集共包含22个文件，总大小约25 GB。样本文件名带有描述性信息，可体现蓝莓类别与编号信息。该数据集曾被用于开发区分正常蓝莓与缺陷蓝莓的机器学习模型，最终实现了96.6%的总体准确率。用于建模工作的软件程序已开源，访问地址为：https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging。
有关本数据集构建与建模实验的详细内容，请参阅以下期刊论文：Deng, B., Lu, Y., Stafne, E. (2024). 融合高光谱反射影像的光谱与空间特征以区分正常蓝莓与缺陷蓝莓. 《智能农业技术》. https://doi.org/10.1016/j.atech.2024.100473。若您在已发表的研究中使用本数据集，请引用本数据集或上述期刊论文。衷心希望本数据集对您的研究有所助益。