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Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning

The widespread use of unmanned aerial vehicles (UAV) is significant for the effective management of orchards in the context of precision agriculture. To reduce the traditional mode of continuous spraying, variable target spraying machines require detailed information about tree canopy. Although deep...

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Autores principales: Zhang, Weirong, Chen, Xuegeng, Qi, Jiangtao, Yang, Sisi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9752849/
https://www.ncbi.nlm.nih.gov/pubmed/36531373
http://dx.doi.org/10.3389/fpls.2022.1041791
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author Zhang, Weirong
Chen, Xuegeng
Qi, Jiangtao
Yang, Sisi
author_facet Zhang, Weirong
Chen, Xuegeng
Qi, Jiangtao
Yang, Sisi
author_sort Zhang, Weirong
collection PubMed
description The widespread use of unmanned aerial vehicles (UAV) is significant for the effective management of orchards in the context of precision agriculture. To reduce the traditional mode of continuous spraying, variable target spraying machines require detailed information about tree canopy. Although deep learning methods have been widely used in the fields of identifying individual trees, there are still phenomena of branches extending and shadows preventing segmenting edges of tree canopy precisely. Hence, a methodology (MPAPR R-CNN) for the high-precision segment method of apple trees in high-density cultivation orchards by low-altitude visible light images captured is proposed. Mask R-CNN with a path augmentation feature pyramid network (PAFPN) and PointRend algorithm was used as the base segmentation algorithm to output the precise boundaries of the apple tree canopy, which addresses the over- and under-sampling issues encountered in the pixel labeling tasks. The proposed method was tested on another miniature map of the orchard. The average precision (AP) was selected to evaluate the metric of the proposed model. The results showed that with the help of training with the PAFPN and PointRend backbone head that AP_seg and AP_box score improved by 8.96% and 8.37%, respectively. It can be concluded that our algorithm could better capture features of the canopy edges, it could improve the accuracy of the edges of canopy segmentation results.
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spelling pubmed-97528492022-12-16 Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning Zhang, Weirong Chen, Xuegeng Qi, Jiangtao Yang, Sisi Front Plant Sci Plant Science The widespread use of unmanned aerial vehicles (UAV) is significant for the effective management of orchards in the context of precision agriculture. To reduce the traditional mode of continuous spraying, variable target spraying machines require detailed information about tree canopy. Although deep learning methods have been widely used in the fields of identifying individual trees, there are still phenomena of branches extending and shadows preventing segmenting edges of tree canopy precisely. Hence, a methodology (MPAPR R-CNN) for the high-precision segment method of apple trees in high-density cultivation orchards by low-altitude visible light images captured is proposed. Mask R-CNN with a path augmentation feature pyramid network (PAFPN) and PointRend algorithm was used as the base segmentation algorithm to output the precise boundaries of the apple tree canopy, which addresses the over- and under-sampling issues encountered in the pixel labeling tasks. The proposed method was tested on another miniature map of the orchard. The average precision (AP) was selected to evaluate the metric of the proposed model. The results showed that with the help of training with the PAFPN and PointRend backbone head that AP_seg and AP_box score improved by 8.96% and 8.37%, respectively. It can be concluded that our algorithm could better capture features of the canopy edges, it could improve the accuracy of the edges of canopy segmentation results. Frontiers Media S.A. 2022-12-01 /pmc/articles/PMC9752849/ /pubmed/36531373 http://dx.doi.org/10.3389/fpls.2022.1041791 Text en Copyright © 2022 Zhang, Chen, Qi and Yang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zhang, Weirong
Chen, Xuegeng
Qi, Jiangtao
Yang, Sisi
Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning
title Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning
title_full Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning
title_fullStr Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning
title_full_unstemmed Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning
title_short Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning
title_sort automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9752849/
https://www.ncbi.nlm.nih.gov/pubmed/36531373
http://dx.doi.org/10.3389/fpls.2022.1041791
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