Published November 20, 2024 | Version v1

Seasonal leaf area data of apple trees (3 years) read by reference LiDAR and lowcost RGB-D sensors

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

Description

Apple trees were scanned with two LiDAR sensors (SICK LMS511 and Pepperl & Fuchs). Both sensors provide 3D point clouds of each tree to gain geometric information. Such geometric information provide the gold standard for leaf area analysis at present. Additionally the SICK LMS511 provides the return signal strength intensity at 905 nm and the P&F the RSSI measured at 660 nm. These intensity values at specific wavelengths allow a dual wavelength 3D point cloud analysis. Such wavelength-dependant analysis can provide information on the fruit ripeness based on the decrease of fruit chlorophyll content.

The third sensor captured is a lowcost sensor: Intel RealSense 435. The RealSense provides RGB and depth information. For each tree 20 RGB images and the depth information is captured.

This example dataset captures a part of a bigger dataset, representing an example of one tree measured from two sides (right R, left L) for three years. The example data were selected randomly (27.09.2021, 07.09.2022, 12.06.2023). 

The entire dataset available at ATB captures the data (examplarily shown here for 1 tree in three years) for ca. 80 trees on each of the following dates, summing up to approx. 1400 trees:

Plot Year Date LiDAR (LMS511 + P&F) (GB) Intel RealSense 435 (GB)
MQT 2021 06.07.2021 7.9 12.7
MQT 2021 12.07.2021 13.2 50.6
MQT 2021 24.08.2021 15.0 48.3
MQT 2021 06.09.2021 15.4 50.1
MQT 2021 27.09.2021 22.8 155.0
MQT 2022 11.05.2022 24.0 48.0
MQT 2022 29.06.2022 38.5 207.0
MQT 2022 12.07.2022 12.8 131.0
MQT 2022 17.08.2022 15.7 79.0
MQT 2022 04.08.2022 17.0 164.0
MQT 2022 07.09.2022 20.4 67.7
MQT 2023 22.08.2023 15.6 529.0
MQT 2023 06.09.2023 84.0 167.0
MQT 2023 03.11.2023 7.6 103.0
MQT 2023 11.04.2024 9.9 68.5
MQT 2024 24.05.2024 35.1 529.0
MQT 2024 03.06.2024 36.9 448.0
MQT 2024 09.07.2024 13.6 451.0

 

Such dataset allows to follow the growth of leaf area (or leaf area index) over the seasonal. The dataset provided opportunities

  • to test various data analysis methods for leaf area estimation, based on manual reference data, which were obtained by defoliation method.
  • to compare the expensive LiDAR with lowcost sensor unit.
  • developing growth curves to interpolate the leaf area for comparing with satellite data, when available.
  • to run various approaches to fuse the data and, e.g. perform dual wavelength analysis in 3D.
  • to test data processing steps for fruit ripeness analysis, based on manual fruit size readings.
  • test own physiological growth models against this open access dataset.

The dataset is given in following formats:

csv files contain one 3D pointcloud of each individual trees measured from two sides, at each measurement date.

csv files contain Sentinel 2 data of the plot. The file name indicates the satellite overpass.

txt files contain depth information by RGB-D sensor measured twenty times of each individual trees measured from two sides, at each measurement date.

bmp files contain RGB images by RGB-D sensor measured 20 times of each individual trees measured from two sides, at each measurement date.

xlsx files capture manual reference analyses of leaf area per tree (n = 37) and fruit (n > 500).

 

Files

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Additional details

Additional titles

Subtitle
Project IRRIWELL (A novel plant-based approach to estimate irrigation water needs and the application of optimal deficit strategy, grant number 01DH21016, 2021-2024) funded through a PRIMA initiative of Members States, Associated Countries, and Participating Countries.
Subtitle
Example data set