Welcome to
the FST-TOMMY CrackSense open access database, capturing
the fruit surface temperature (FST) of various crops and different climates. In
recent years, due to increase in population and climate change, increasing
efforts have been made on developing resilient approaches to minimize food
waste along the supply chain. The impact of climatic changes, hereof,
increasing temperature, are prone to be among the main triggers of
physiological disorders and abiotic stresses, generating losses in pre and
post-harvest scenarios of horticultural produce.
Previously,
monitoring temperature at the fruit level in orchards has been enabled with stationary
thermocouples on few fruit and simulations based on weather data with low
spatial resolution. More recently, data at canopy and fruit level have been
achieved, employing close range remote sensing approaches and sensor data fusion
based on a Terrestrial Orchard Monitoring and Measuring arraY (TOMMY) that has been used to scan canopies in various
climates.
Each entry
of the database represents a dataset of an individual fruit. The entry captures
the mean temperature of individual fruit, considering several temperature
points per fruit, and the range of temperature variation found on the surface
of this fruit. Supplementary data for each entry capture meta
data on the plot, weather data from local weather station, and raw data for the
FST calculation (temperature-annotated pointclouds of
entire canopies and individual fruit).
Data contributions
are from CrackSense partners: INSTITUT NATIONAL DE
RECHERCHE POUR L'AGRICULTURE, L'ALIMENTATION ET L'ENVIRONNEMENT (INRAe); LEHR- UND VERSUCHSANSTALT FÜR GARTENBAU UND
ARBORISTIK E.V. (LVGA); THE AGRICULTURAL RESEARCH ORGANISATION OF ISRAEL - THE
VOLCANI CENTRE (ARO); LEIBNIZ-INSTITUT FUR AGRARTECHNIK UND BIOOKONOMIE EV
(ATB); AGROTIKOS SYNETAIRISMOS POLISEOS XIRON KAI NOPON STAFYLION KIATOY
KORINTHIAS PIGASOS (PEGASUS).
This
research was funded by European Horizon2020 RIA program, project “CrackSense“, grant number
101086300.
The data are recorded in the framework of European CrackSense project and are, therefore, made available here according to the FAIR guidelines of European Union for reuse under the license CC BY-NC-ND 4.0 (Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International License). With this license, users
• can share the data in any medium or format for non-commercial purposes;
• cannot alter and share such altered data or derived results from altered data;
• must give reference to this data repository as follows and include a link to the license.
Tapia Zapata, N.; Regen, C.; Quero-Garcia, J.; Fountas, S.; Alchanatis, V.; Zude-Sasse, M. Temperature Annotated 3D Point Clouds Measured in Various Climate Conditions, Providing the Fruit Surface Temperature and Local Weather Data. ATB. 2025. Available online: https://technologygarden.atb-potsdam.de/cracksense/
Each
dataset uses the following nomenclature:
|
ID
|
Running specific number
|
|
Image
|
Photo of the measuring scene
|
|
DateTime
|
Date and time of the measurement in the field
|
|
UTC
|
Difference of Date/Time compared to coordinated universal time
|
|
Plot
|
Country code and place of the measurement
|
|
|
GPS from Google Maps
|
|
Crop
|
Species
|
|
Cultivar
|
Variety or cultivar
|
|
Tree label
|
Any additional label according to nomenclature of the data provider
|
|
Fruit label
|
Any additional label according to nomenclature of the data provider
|
|
Free text
|
Remarks from the data provider
|
|
x [m]
|
Position of fruit considering the x axis in the local canopy scan
|
|
y [m]
|
Position of fruit considering the y axis in the local canopy scan
|
|
z [m]
|
Position of fruit considering the z axis in the local canopy scan
|
|
r [m]
|
Euclidean distance between LiDAR origin and fruit centroid
|
|
 (rad)
|
Angular coordinate with respect to the LiDAR position
|
|
Fruit size [mm]
|
Estimated fruit size (based on LiDAR data)
|
|
![Fruit Tmean [C°]](CrackSense_FSTTOMMY_intro-Dateien/image005.png)
|
Mean fruit surface temperature (based on T-annotated LiDAR data)
|
|
![Fruit dT [K]](CrackSense_FSTTOMMY_intro-Dateien/image006.png)
|
Difference of fruit surface temperature within the fruit or fruit
cluster, considering the interquartile temperature range (iqr)
(based on T-annotated LiDAR data).
|
|
![Canopy Tmean [C°]](CrackSense_FSTTOMMY_intro-Dateien/image007.png)
|
Mean canopy surface temperature (based on T-annotated LiDAR data)
|
|
![Canopy dT [K]](CrackSense_FSTTOMMY_intro-Dateien/image008.png)
|
Difference of canopy surface temperature, considering the interquartile
temperature range (iqr) (based on T-annotated LiDAR
data)
|
|
Tair [°C]
|
Air temperature at 1 m by local weather station, or indicated otherwise
in the free text comments
|
|
Wind speed [m/s]
|
Wind speed measured above the canopies by local weather station
|
|
Relative humidity [%]
|
Relative humidity measured at 1 m by local weather station
|
|
Global radiation [W/m²]
|
Global radiation measured above the canopies by local weather station
|
|
Precipitation [mm]
|
Rainfall measured above the canopies by local weather station
|
|
Pointcloud
|
Raw data: T-annotated 3D LiDAR pointcloud of
the canopy in which the fruit is located. The 3D pointcloud
provides the geometric information of the canopy in Cartesian coordinates and
the return signal strength intensity (RSSI) of each point. The RSSI values
can be transformed to reflectivity using a calibration file, which is
available on request.
|
|
Image
|
High resolution image of the canopy, in which the fruit is located
|
|
Upload TimeDate
|
Time and date, when the entry was uploaded to this database
|
|
|
|
|
Additional columns in the csv download
|
Name of the T-annotated 3D LiDAR point cloud of the canopy or zip file
with canopy and individual fruit pointclouds
Name of the high resolution image of the canopy, in which the fruit is
located
|
