Published February 21, 2024 | Version v2
Dataset Open

Temperature annotated 3D Point Cloud of sweet cherry trees in climate chamber

  • 1. ROR icon Leibniz Institute for Agricultural Engineering and Bioeconomy
  • 2. ROR icon Leibniz University Hannover

Description

Measurements were carried out at Leibniz Universität Hannover, using terrestrial orchard monitoring measurement yaw (TOMMY) developed by Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB), in 2023.

Seven sweet cherry (Prunus avium) trees were allowed to acclimate to ambient temperature and then placed in cold conditions. Tree temperature was measured with a handheld IR thermometer and analysis based on fused data from terrestrial mobile 2D LiDAR sensor analysis and thermal camera to gain tree point clouds with temperature labels. Tree temperature acclimation data are provided here as an preliminary example file showing forced wetness appearance on the fruit surface, capturing fruit temperature and wetness reference data as well as T-annotated point clouds. 

Sensor

Specifications

LiDAR sensor (LRS4000, Sick AG, Waldkirch, Germany)

Wavelength: 905 nm, angular resolution: 0.02°, scanning frequency: 25 Hz, scanning angle: 180°, laser aperture: 13.6 mm

Thermal camera (A655sc, FLIR Systems Inc., MA, USA) with lens (T198065, FLIR Systems Inc., MA, USA)

spatial resolution: 640 × 480 pixels at 50 Hz, spectral range: 7.5 – 14.0 μm, focal length: 6.5 mm, detects thermal differences < 30 mK

IR-Thermometer (Microscanner D501, Exergen, Watertown, USA)

Operational temperature: 0 - 50 °C, resolution: 0.1 °C, rms error: +/- 1 %, repeatability: 0.01 °C

Files:

*.xlsx file contain

  • several clusters of fruit or single fruit during acclimation, representing geometric x, y, z data; return signal strength intensity, temperature
  • remaining point clouds
  • reference readings capturing manual temperature and wetness ground truth as well as temperature estimated by TOMMY

Python code to read files.

A total of 549 RGB images of cherry fruit, usable for identifying the fruit, where visual reference for wetness classes was recorded. Images are available per tree (T1-7) in zip files.

Manually measured fruit surface temperature and visually classed wetness level are provided in CrackSense_ReferenceMeasurementTOMMY_Hannover.xlsx file.

Notes

CrackSense - High throughput real-time monitoring and prediction of fruit cracking by utilising and upscaling sensing and digital data technologies”, REA - HORIZON-CL6-2022-GOVERNANCE-01-11, Grant Agreement: 101086300

Deliverable 2.3.

Files

T1_1307_0830_leftover.csv

Files (3.1 GB)

Name Size Download all
md5:f1faf87aa0981ae709413708d4227fa3
17.1 MB Download
md5:ecc1e8ae14f78aa9b3188ff6ff6f0eb5
5.3 MB Download
md5:d1b944a62f6d3e32c4d4741f2fd9704f
7.8 MB Download
md5:01f7ff142225a2aa0f98b8dbf9b830e6
12.9 MB Download
md5:f556239db8ee3ca63116bfcd271429c1
6.2 MB Download
md5:adab0594697a9de2e8ec32bbef840e04
52.4 MB Download
md5:0e883b3c5a46048430cf6c06152b84c7
20.8 MB Download
md5:3801716e345be0d729e24ac5b9814b91
457 Bytes Download
md5:9e6cc07ee791b71b5fb7c2e01b20966c
867.3 kB Download
md5:c3911bde3ccd9100ece04a463f8f64c6
114.2 MB Preview Download
md5:4428ae30818749adbd077f5fd6af3a4b
117.2 MB Preview Download
md5:5f9b7533319d9f03f3a693970927efce
114.1 MB Preview Download
md5:89f8cb95e0f7d65e2cf2ceb094d626a0
113.8 MB Preview Download
md5:cde1d68ba5af2e0adc6e947d3e4ce246
92.6 MB Preview Download
md5:9bc39527ec2aa9030c43327993740c4f
92.3 MB Preview Download
md5:113b0bcca314459e1cdd861715f90c79
93.3 MB Preview Download
md5:7ab4276323dcae9785f992dd6e8bf51c
130.1 MB Preview Download
md5:ab63aef8ee6b3f764df9234007c916ae
129.5 MB Preview Download
md5:397be6ee1526f6d65311deda412f4e10
102.8 MB Preview Download
md5:0784c1e6f2de3e969fb927e1b4043506
102.8 MB Preview Download
md5:7de8842b0f7ac3437ade5e084fb7d0f5
102.6 MB Preview Download
md5:3a42b68d46390b3ab92127e698d6bbcc
101.3 MB Preview Download
md5:4385b77c581a3599f350fd108dbb0ab7
102.2 MB Preview Download
md5:811f12f81346fbbd0c545d1b1e38a9c6
102.2 MB Preview Download
md5:cbf2b9ddd8c151ffa3dc1a761b4bffba
100.0 MB Preview Download
md5:6522631d6161f37819f3eb34ad0c22ac
94.0 MB Preview Download
md5:3e9a05dd69e2226a0058d1488a9ad72d
91.8 MB Preview Download
md5:1532a9b524791978d3b12efdce00864b
94.3 MB Preview Download
md5:729b6b2f6710af43b2fb0ec1d3aa996c
95.1 MB Preview Download
md5:e3e52ad5217213a183859b0983b73214
97.2 MB Preview Download
md5:afc901389881ae33a57c32278fd1fb1a
120.4 MB Preview Download
md5:33d5eee4a216cb1efc9528eb5b8e4e33
121.6 MB Preview Download
md5:3485773d57cbbd256e436800ae86f4f1
120.2 MB Preview Download
md5:775aebef3fbe74f22c0eeb6007fae971
119.8 MB Preview Download
md5:7fd3b4d405de16e03e3c444af63ae3de
121.8 MB Preview Download
md5:f5a766d2e3501687ac2426c60863ad6b
119.4 MB Preview Download
md5:e0e041576591fb951b0224c8212d7577
5.9 MB Preview Download
md5:3d7389e4593f49bdb081eec8a8a41124
4.1 MB Preview Download
md5:618323941bf01d0483d1a2d643b9ad4a
2.9 MB Preview Download
md5:a23285c910ee010fe78ded0e9ba75a86
4.0 MB Preview Download
md5:57b2d576a1006b7aac94a587d8b9b5db
2.4 MB Preview Download
md5:eaf774360ea6e86f2f2e296502d15d6c
6.5 MB Preview Download
md5:5df52a4a2feb540cbbf56cf9902907e1
6.9 MB Preview Download