A LiDAR-based machine vision dataset for online volume measurement of sweetpotatoes.

Abstract

Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement are, however, laborious, destructive, and unsuitable for high-throughput online scenarios. To address this gap, this dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system. A total of 200 sweetpotato storage roots of the cultivar "Beauregard" were collected for constructing a 3-D multi-view imagery dataset. Each sample was imaged online using a short-range LiDAR camera (Intel RealSense™ L515) while traveling on a custom-built roller conveyor system that enables simultaneous translation and rotation for full-surface coverage. The curated dataset comprises raw color images (1280 × 720 pixels, .png format) and corresponding raw and segmented point clouds (1280 × 720 pixels, .laz format) for individual samples, alongside the reference volume measurements obtained using the standard water displacement method. In addition, to illustrate the modeling pipeline for volume prediction, the dataset provides the extracted geometric features derived from the segmented two-dimensional (2-D) masks and point clouds, and volume prediction results obtained through regression modeling. As the first publicly available LiDAR-based dataset for sweetpotato volume estimation, this dataset provides a valuable resource for developing and validating image processing pipelines, optimizing machine learning models, and advancing 3-D vision technologies for non-destructive, rapid measurement of the volume of irregularly shaped agricultural products.

Plant phenotyping relevance

サツマイモ貯蔵根の体積という植物器官形質をLiDAR 3D画像から推定する公開データセットであり、取得系・参照測定・特徴抽出・予測結果を含むため、フェノタイピング手法とデータセットが中心です。

Code and data availability

The paper's own LiDAR sweetpotato dataset (images, point clouds, ground-truth volumes, feature data, and Python modeling scripts) is publicly deposited on Zenodo with an explicit DOI. The librealsense GitHub link is a generic camera SDK, not a paper-specific asset.