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Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks

During the process of drought and rehydration, dew can promote the rapid activation of photosynthetic activity and delay the wilting time of plant leaves and stems. It is clear that the amount of dew will affect the growth of plants. However, limited research is being done to detect and measure the...

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Autores principales: Lv, Meibo, Zhou, Pengyao, Yu, Tong, Wang, Wuwei, Zhou, Daming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081877/
https://www.ncbi.nlm.nih.gov/pubmed/35548293
http://dx.doi.org/10.3389/fpls.2022.861534
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author Lv, Meibo
Zhou, Pengyao
Yu, Tong
Wang, Wuwei
Zhou, Daming
author_facet Lv, Meibo
Zhou, Pengyao
Yu, Tong
Wang, Wuwei
Zhou, Daming
author_sort Lv, Meibo
collection PubMed
description During the process of drought and rehydration, dew can promote the rapid activation of photosynthetic activity and delay the wilting time of plant leaves and stems. It is clear that the amount of dew will affect the growth of plants. However, limited research is being done to detect and measure the amount of dew. Therefore, in this study, a statistical method for measuring the amount of dew based on computer vision processing was developed. In our framework, dewdrops can be accurately measured by isolating the background area based on color features and detecting the edge and statistical area. In this scheme, the multi-convolutional edge detection networks based on contour search loss function are proposed as the main implementation algorithm of edge detection. Through color feature background region segmentation and the proposed edge detection networks, our algorithm can detect dew in complex plant backgrounds. Experimental results showed that the proposed method gains a favorable detection accuracy compared with other edge detection methods. Moreover, we achieved the best Optimal Image Scale (OIS) and Optimal Dataset Scale (ODS) when testing with different pixel values, which illustrate the robustness of our method in dew detection.
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spelling pubmed-90818772022-05-10 Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks Lv, Meibo Zhou, Pengyao Yu, Tong Wang, Wuwei Zhou, Daming Front Plant Sci Plant Science During the process of drought and rehydration, dew can promote the rapid activation of photosynthetic activity and delay the wilting time of plant leaves and stems. It is clear that the amount of dew will affect the growth of plants. However, limited research is being done to detect and measure the amount of dew. Therefore, in this study, a statistical method for measuring the amount of dew based on computer vision processing was developed. In our framework, dewdrops can be accurately measured by isolating the background area based on color features and detecting the edge and statistical area. In this scheme, the multi-convolutional edge detection networks based on contour search loss function are proposed as the main implementation algorithm of edge detection. Through color feature background region segmentation and the proposed edge detection networks, our algorithm can detect dew in complex plant backgrounds. Experimental results showed that the proposed method gains a favorable detection accuracy compared with other edge detection methods. Moreover, we achieved the best Optimal Image Scale (OIS) and Optimal Dataset Scale (ODS) when testing with different pixel values, which illustrate the robustness of our method in dew detection. Frontiers Media S.A. 2022-04-25 /pmc/articles/PMC9081877/ /pubmed/35548293 http://dx.doi.org/10.3389/fpls.2022.861534 Text en Copyright © 2022 Lv, Zhou, Yu, Wang and Zhou. 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
Lv, Meibo
Zhou, Pengyao
Yu, Tong
Wang, Wuwei
Zhou, Daming
Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks
title Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks
title_full Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks
title_fullStr Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks
title_full_unstemmed Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks
title_short Leaf and Stem-Based Dew Detection Algorithm via Multi-Convolutional Edge Detection Networks
title_sort leaf and stem-based dew detection algorithm via multi-convolutional edge detection networks
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081877/
https://www.ncbi.nlm.nih.gov/pubmed/35548293
http://dx.doi.org/10.3389/fpls.2022.861534
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