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Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

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 Spai...

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Autores principales: Safonova, Anastasiia, Guirado, Emilio, Maglinets, Yuriy, Alcaraz-Segura, Domingo, Tabik, Siham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956790/
https://www.ncbi.nlm.nih.gov/pubmed/33668984
http://dx.doi.org/10.3390/s21051617
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author Safonova, Anastasiia
Guirado, Emilio
Maglinets, Yuriy
Alcaraz-Segura, Domingo
Tabik, Siham
author_facet Safonova, Anastasiia
Guirado, Emilio
Maglinets, Yuriy
Alcaraz-Segura, Domingo
Tabik, Siham
author_sort Safonova, Anastasiia
collection PubMed
description 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.
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spelling pubmed-79567902021-03-16 Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN Safonova, Anastasiia Guirado, Emilio Maglinets, Yuriy Alcaraz-Segura, Domingo Tabik, Siham Sensors (Basel) Article 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. MDPI 2021-02-25 /pmc/articles/PMC7956790/ /pubmed/33668984 http://dx.doi.org/10.3390/s21051617 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Safonova, Anastasiia
Guirado, Emilio
Maglinets, Yuriy
Alcaraz-Segura, Domingo
Tabik, Siham
Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN
title Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN
title_full Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN
title_fullStr Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN
title_full_unstemmed Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN
title_short Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN
title_sort olive tree biovolume from uav multi-resolution image segmentation with mask r-cnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956790/
https://www.ncbi.nlm.nih.gov/pubmed/33668984
http://dx.doi.org/10.3390/s21051617
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