3D LiDAR point cloud dataset for moisture-level classification in broccoli and mushroom

Broccoli and mushroom samples (fresh and dried) were measured with light detection and ranging (LiDAR) sensor. The dual-wavelength LiDAR system for acquisition of 3D geometry and intensity data of samples, was configured with 1320 nm and 1450 nm. These two wavelengths were chosen to measure at one low sensitive wavelength and one wavelength, showing high sensitivity to the presence of water. Moisture content was measured after each LiDAR scan for the core and periphery segments of samples to capture spatial variability. Each sample was weighed directly after the scan and after oven drying until constant weight was reached, and the moisture content (MC) was calculated. Data are available as raw data with trajectories as well as data segmented on the individual samples and radiometrically calibrated by means of low and high reflecting standard materials. Data were augmented and moisture classification can be done with code provided with link below.采用激光雷达（LiDAR）传感器对西兰花与蘑菇样本（包括新鲜与烘干样本）开展了测量。本研究采用的双波长激光雷达系统可采集样本的三维几何与强度数据，其配置的激光波长为1320 nm与1450 nm。本次选用这两个波长，分别为低灵敏度波长与对水分存在具有高灵敏度的波长。在每次激光雷达扫描结束后，针对样本的核心区域与边缘区域分别测定含水率，以捕捉样本的空间变异特性。对每份样本，分别在扫描完成后即刻称重，以及置于烘箱中烘干至恒重后称重，据此计算得到样本的含水率（MC）。数据集包含带有扫描轨迹的原始数据，以及经单样本分割得到、且通过高低反射率标准材料完成辐射定标的数据。本数据集已完成数据增强处理，且可通过下方附带链接的代码实现含水率分类任务。