A LiDAR-based Machine Vision Dataset for Online Volume Measurement of Sweetpotatoes

Dataset Structure```SP_3D_Dataset.zip├─ raw_PointClouds│ ├─ SampleID_001│ │ ├─ ...│ │ ├─ SampleID_001_FrameID_0xx.laz│ │ └─ ...│ ├─ ...│ └─ SampleID_200│├─ segmented_PointClouds│ ├─ SampleID_001│ │ ├─ ...│ │ ├─ SampleID_001_FrameID_0xx.laz│ │ └─ ...│ ├─ ...│ └─ SampleID_200│├─ selected_Images│ ├─ SampleID_001│ │ ├─ ...│ │ ├─ SampleID_001_FrameID_0xx.png│ │ └─ ...│ ├─ ...│ └─ SampleID_200│└─ sweetpotato_volume_ground-truth.xlsx```--- Dataset OrganizationWhen extracting the archive, the dataset is organized into three primary functional folders and one ground-truth file: **raw_PointClouds**: Contains raw 3D point cloud data (.laz format) directly acquired from the LiDAR sensor. **segmented_PointClouds**: Contains cleaned point cloud data after background removal and statistical denoising. **selected_Images**: Contains synchronized 2D RGB images (.png). **sweetpotato_volume_ground-truth.xlsx**: A spreadsheet containing physical reference measurements for all 200 samples. Directory HierarchyThe dataset follows a consistent hierarchical structure across all directories: Each primary folder contains 200 subfolders (labeled `SampleID_001` to `SampleID_200`), corresponding to specific sweetpotato samples. Image Resolution: RGB and Depth maps are 1280 × 720 pixels. Temporal Consistency: The original frame indices from the raw recording were preserved to allow for multi-view fusion and tracking research. Dataset Summary Total Samples: 200 "Beauregard" sweetpotatoes. Imaging System: Custom LiDAR-based roller conveyor (Intel RealSense L515). Reference Method: Standard water displacement method (average of two replicates). Storage Space: Approximately 15.5 GB (uncompressed). Camera & Imaging SpecificationsAs requested by the system configuration, the Intel RealSense L515 LiDAR was operated with the following settings to ensure data consistency: | Parameter | Configuration / Value ||-----------------------|-----------------------------------------------------------|| **Sensor Model** | Intel RealSense™ L515 LiDAR || **RGB Resolution** | 1280 × 720 pixels (.png) || **Depth Resolution** | 1280 × 720 pixels (.ply) || **Frame Rate** | 30 FPS || **Laser Wavelength** | 860 nm || **Mounting Height** | 0.43 m above the conveyor || **Imaging Lighting** | Ambient indoor light (no controlled lighting) || **Conveyor Speed** | 10 mm/s|| **Exposure Time** | 1250 $\mu s$|| **Gain** | 10|| **Brightness** | 1|| **Contrast** | 50|| **Backlight Compensation** | 98|| **Saturation** | 50|| **Sharpness** | 80|| **White Balance** | 4600 K| File Naming ConventionThe naming convention ensures traceability and temporal alignment:`SampleID_[ID]_FrameID_[ID].[ext]`1. **SampleID**: Unique identifier for each physical sweetpotato root.2. **FrameID**: Sequential order of the frame extracted from the continuous recording. Example: `SampleID_001_FrameID_015.png` is the 15th frame of the 1st sample. Data Processing & Feature Extraction Segmentation: Binary masks were generated using HSV thresholding ($T_{min}=[7,40,120]$, $T_{max}=[20,165,219]$), flood-filling, and morphological opening. Denoising: Statistical Outlier Removal (SOR) was applied using 50 nearest neighbors and a 0.02 standard deviation ratio. Potential Usage: The high-density point clouds support the extraction of 2D features (area, perimeter, radial distance) and 3D features (projected volume, surface area) as described in the related research. Demo A minimal workflow is provided for readers to validate the data; see the `Demo` folder for details. In the (unzipped) demo folder, the spreadsheet "Ft.csv" contains a feature set for 200 samples, each comprising 6 extracted frames and 8 features per RGB-D frame, that is, a 1200 x 8 feature matrix, while the spreadsheet "y.csv" contains the corresponding volume ground-truth. The feature and ground-truth data are also saved in "Ft.mat" and "y.mat" Matlab files, respectively, in the "Demo" folder. To demonstrate the volume prediction, features for samples 1-134 were used for model development and samples 135-200 for testing, as described in Xu et al. (2024). The Python script `data_loader.py` is included for Synchronized 2D/3D dataset traversing, and the "SW_VOL_PRED_MLR_PCA.py" script is used for volume modeling and prediction using simple methods, i.e., multiple linear regression (MLR) and principal component regression (principal component analysis + MLR). The models achieved accuracy comparable to that reported in the related paper (Xu et al., 2024). The model results are reported in Zhang et al. (2026b). CitationsIf you use this dataset in your research, please cite the following: **Related Research Article:**Xu, J., Lu, Y., Olaniyi, E., & Harvey, L. (2024). Online volume measurement of sweetpotatoes by a LiDAR-based machine vision system. *Journal of Food Engineering*, 361, 111725. https://doi.org/10.1016/j.jfoodeng.2023.111725 **Dataset:**Zhang, J., Lu, Y., Kong, Z., & Xu, J. (2026a). A LiDAR-based Machine Vision Dataset for Online Volume Measurement of Sweetpotatoes [Data set]. *Zenodo*. https://doi.org/10.5281/zenodo.18378019. Zhang, J., Lu, Y., Kong, Z., & Xu, J. (2026). A LiDAR-based Machine Vision Dataset for Online Volume Measurement of Sweetpotatoes. Data in Brief (pending). ---*For questions regarding the data collection or system configuration, please contact [e-mail].*## 数据集结构 SP_3D_Dataset.zip ├─ raw_PointClouds │ ├─ SampleID_001 │ │ ├─ ... │ │ ├─ SampleID_001_FrameID_0xx.laz │ │ └─ ... │ ├─ ... │ └─ SampleID_200 ├─ segmented_PointClouds │ ├─ SampleID_001 │ │ ├─ ... │ │ ├─ SampleID_001_FrameID_0xx.laz │ │ └─ ... │ ├─ ... │ └─ SampleID_200 ├─ selected_Images │ ├─ SampleID_001 │ │ ├─ ... │ │ ├─ SampleID_001_FrameID_0xx.png │ │ └─ ... │ ├─ ... │ └─ SampleID_200 └─ sweetpotato_volume_ground-truth.xlsx --- ## 数据集组织 解压归档后，本数据集包含3个核心功能文件夹与1个真值文件： 1. **原始点云数据集（raw_PointClouds）**：存储由激光雷达（LiDAR）传感器直接采集的原始三维点云数据，格式为.laz。 2. **分割后点云数据集（segmented_PointClouds）**：存储经过背景去除与统计降噪处理后的洁净点云数据。 3. **筛选图像集（selected_Images）**：存储与点云同步的二维RGB图像，格式为.png。 4. **甘薯体积真值表.xlsx（sweetpotato_volume_ground-truth.xlsx）**：包含全部200个样本的物理参考测量值的电子表格。 ## 目录层级结构 所有文件夹均遵循统一的层级逻辑： 每个主文件夹下均包含200个子文件夹，命名为`SampleID_001`至`SampleID_200`，分别对应不同的甘薯样本。 - 图像分辨率：RGB与深度图分辨率均为1280 × 720像素。 - 时间一致性：保留原始录制的帧索引，以支持多视图融合与跟踪相关研究。 ## 数据集概况 - 总样本量：200个“博雷加德（Beauregard）”甘薯样本。 - 成像系统：定制激光雷达（LiDAR）滚筒输送机系统，搭载英特尔实感（Intel RealSense）L515传感器。 - 参考测量方法：标准排水法（两次重复测量取平均值）。 - 存储空间：未压缩状态下约占15.5 GB。 ## 相机与成像参数 为保证数据一致性，英特尔实感L515激光雷达（LiDAR）按照以下参数运行： | 参数项 | 配置/取值 | |-----------------------|--------------------------------------------------------------------------| | **传感器型号** | 英特尔实感（Intel RealSense™）L515激光雷达（LiDAR） | | **RGB分辨率** | 1280 × 720像素，存储格式为.png | | **深度分辨率** | 1280 × 720像素，存储格式为.ply | | **帧率** | 30 FPS | | **激光波长** | 860 nm | | **安装高度** | 输送机上方0.43米 | | **成像光照** | 室内环境自然光（无额外受控照明） | | **输送机速度** | 10 mm/s | | **曝光时长** | 1250 μs | | **增益** | 10 | | **亮度** | 1 | | **对比度** | 50 | | **背光补偿** | 98 | | **饱和度** | 50 | | **锐度** | 80 | | **白平衡** | 4600 K | ## 文件命名规范 命名规则确保数据可追溯性与时间对齐性，格式为：`SampleID_[样本编号]_FrameID_[帧编号].[扩展名]` 1. **SampleID**：每个甘薯块根的唯一标识符。 2. **FrameID**：从连续录制视频中提取的帧的顺序编号。 示例：`SampleID_001_FrameID_015.png`代表第1号样本的第15帧图像。 ## 数据处理与特征提取 1. **分割处理**：通过HSV阈值分割（最小阈值$T_{min}=[7,40,120]$，最大阈值$T_{max}=[20,165,219]$）、泛洪填充与形态学开运算生成二值掩码。 2. **降噪处理**：采用统计离群值移除（SOR）算法，使用50个近邻点与0.02的标准差比例进行降噪。 3. **潜在应用场景**：高密度点云可用于提取二维特征（面积、周长、径向距离）与三维特征（投影体积、表面积），相关研究已对此进行了详细阐述。 ## 演示示例 为方便使用者验证数据，本数据集附带了极简工作流，详情请查看`Demo`文件夹。解压后的演示文件夹中，电子表格`Ft.csv`包含200个样本的特征集：每个样本对应6个提取帧，每帧RGB-D图像包含8个特征，最终形成1200 × 8的特征矩阵；电子表格`y.csv`则包含对应的体积真值数据。此外，演示文件夹中还分别以Matlab文件`Ft.mat`与`y.mat`存储了特征与真值数据。 按照Xu等人（2024）的研究方案，本演示使用样本1-134的数据进行模型开发，样本135-200的数据作为测试集以实现体积预测。附带的Python脚本`data_loader.py`用于遍历同步的二维/三维数据集，`SW_VOL_PRED_MLR_PCA.py`脚本则使用简单方法（多元线性回归MLR与主成分回归，即主成分分析+MLR）实现体积建模与预测。模型的预测精度与相关论文（Xu et al., 2024）中报道的结果相当，模型结果已在Zhang等人（2026b）中发表。 ## 引用说明 若您在研究中使用本数据集，请引用以下文献： ### 相关研究论文 Xu, J., Lu, Y., Olaniyi, E., & Harvey, L. (2024). Online volume measurement of sweetpotatoes by a LiDAR-based machine vision system. *Journal of Food Engineering*, 361, 111725. https://doi.org/10.1016/j.jfoodeng.2023.111725 ### 数据集文献 1. Zhang, J., Lu, Y., Kong, Z., & Xu, J. (2026a). A LiDAR-based Machine Vision Dataset for Online Volume Measurement of Sweetpotatoes [Data set]. *Zenodo*. https://doi.org/10.5281/zenodo.18378019. 2. Zhang, J., Lu, Y., Kong, Z., & Xu, J. (2026). A LiDAR-based Machine Vision Dataset for Online Volume Measurement of Sweetpotatoes. Data in Brief (pending). --- *若您对数据采集或系统配置有疑问，请联系 [e-mail]。*