A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis

The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of...

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Bibliographic Details
Published in:Agronomy (Basel) 2022-01, Vol.12 (1), p.43
Main Authors: Ponce, Juan Manuel, Aquino, Arturo, Tejada, Diego, Al-Hadithi, Basil Mohammed, Andújar, José Manuel
Format: Article
Language:English
Subjects:
aerial imagery
Aerial photography
Agricultural production
Agriculture
Automation
Canopies
canopy cover
Citrus fruits
Coverage
Crop fields
crop tree
Crops
Deep learning
Delineation
Farmers
Image analysis
Image processing
Methodology
morphological image analysis
Morphology
Orchards
Photogrammetry
Plant populations
Remote sensing
Representations
Seeds
Trees
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
Vegetation
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Summary:The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy12010043