@misc{10481/67986,
year = {2021},
url = {http://hdl.handle.net/10481/67986},
abstract = {Olive tree growing is an important economic activity in many countries, mostly in the
Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification
techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are
scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate
measurement of trees biovolume is a first step to monitor their performance in olive production and
health. In this work, we use one of the most accurate deep learning instance segmentation methods
(Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow
segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our
approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation
indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation
index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial
resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel.
All trained Mask R-CNN-based models showed high performance in the tree crown segmentation,
particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%).
The comparison in a subset of trees of our estimated biovolume with ground truth measurements
showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral
indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV
images.},
organization = {Russian Foundation for Basic Research (RFBR)

19-01-00215
20-07-00370},
organization = {European Research Council (ERC)

European Commission

647038},
organization = {Spanish Government

RYC-2015-18136},
organization = {Consejeria de Economia, Conocimiento y Universidad de la Junta de Andalucia 

P18-RT-1927},
organization = {DETECTOR 

A-RNM-256-UGR18},
organization = {European Research and Development Funds (ERDF) program},
publisher = {MDPI},
keywords = {Instance segmentation},
keywords = {Machine learning},
keywords = {Deep neural networks},
keywords = {Olive tree},
keywords = {Ultra-high resolution images},
title = {Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN},
doi = {10.3390/s21051617},
author = {Safonova, Anastasiia and Alcaraz Segura, Domingo},
}