Automatic Detection of Olive Tree Canopies for Groves with Thick Plant Cover on the Ground

Marking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to build the models whose predictions are compared with the g...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-08, Vol.22 (16), p.6219
Main Authors: Illana Rico, Sergio, Martínez Gila, Diego Manuel, Cano Marchal, Pablo, Gómez Ortega, Juan
Format: Article
Language:English
Subjects:
Algorithms
Canopies
Corn
Delaunay triangulation
Distribution
Factories
High resolution
high-resolution aerial images
Identification and classification
Marking
multispectral imagery
Multispectral photography
Olive
Olive oil
olive tree canopy
Olives
Plant canopies
precision agriculture
remote sensing
Satellites
Seeds
Sensors
Spatial data
Trees
Unmanned aerial vehicles
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Summary:Marking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to build the models whose predictions are compared with the ground truth obtained by measurements made with other types of sensors. Marking these canopies manually is an arduous and tedious process that is replaced by automatic methods that rarely work well for groves with a thick plant cover on the ground. This paper develops a standard method for the detection of olive tree canopies from high-resolution aerial images taken by a multispectral camera, regardless of the plant cover density between canopies. The method is based on the relative spatial information between canopies.The planting pattern used by the grower is computed and extrapolated using Delaunay triangulation in order to fuse this knowledge with that previously obtained from spectral information. It is shown that the minimisation of a certain function provides an optimal fit of the parameters that define the marking of the trees, yielding promising results of 77.5% recall and 70.9% precision.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22166219