3D Point Clouds of Trees and Apple Fruit Annotated with Thermal Data

<p>The data set captures four measurements during fruit growth:&nbsp; 06/28/2022 (15:00), 07/12/2022 (15:00), 09/01/2022 (15:00), 09/06/2022 (13:00)</p> <p>Additionally, diurnal courses are provided for three days:&nbsp;&nbsp;</p> <table> <tbody> <tr> <td> <p>Date</p> </td> <td> <p>Time</p> </td> </tr> <tr> <td> <p>09/21</p> </td> <td> <p>07:00, 08:00, 10:00, 12:00, 13:00, 18:00</p> </td> </tr> <tr> <td> <p>09/22</p> </td> <td> <p>07:00, 08:00, 10:00, 12:00, 13:00, 18:00</p> </td> </tr> <tr> <td> <p>10/05</p> </td> <td> <p>06:30, 07:00, 09:00, 10:00, 11:00, 16:00</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p>The data set was measured in Blocks (A-D) of trees (T) on apple (A) fruit and is stored as compressed zip files, capturing raw, preprocessed, and manually recorded reference (ground truth) data.</p> <p>1. zip files entitled Raw_YYYY_MM_DD_Block[A-C]-[R, L] for seasonal data and Raw_YYYY_MM_DD_BlockD-[R, L]_Hour__:__ for diel data</p> <p>- raw data of LiDAR 3D point clouds - txt files</p> <p>- raw image data by thermal camera - txt files</p> <p>2. zip files entitled YYYY_MM_DD or DailyAcquisitions:</p> <p>- preprocessed (merged) sensor data of temperature-annotated 3D point clouds of canopies - csv files</p> <p>- preprocessed data, capturing manually segmented point clouds of temperature-annotated fruit - txt files</p> <p>3. Microsoft Excel files entitled References and Weather data:</p> <p>- raw data, representing reference data of fruit - xlsx file</p> <p>- raw data of weather conditions - xlsx file</p>