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A method of cotton root segmentation based on edge devices

The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems...

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Autores principales: Yu, Qiushi, Tang, Hui, Zhu, Lingxiao, Zhang, Wenjie, Liu, Liantao, Wang, Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982017/
https://www.ncbi.nlm.nih.gov/pubmed/36875594
http://dx.doi.org/10.3389/fpls.2023.1122833
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author Yu, Qiushi
Tang, Hui
Zhu, Lingxiao
Zhang, Wenjie
Liu, Liantao
Wang, Nan
author_facet Yu, Qiushi
Tang, Hui
Zhu, Lingxiao
Zhang, Wenjie
Liu, Liantao
Wang, Nan
author_sort Yu, Qiushi
collection PubMed
description The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.
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spelling pubmed-99820172023-03-04 A method of cotton root segmentation based on edge devices Yu, Qiushi Tang, Hui Zhu, Lingxiao Zhang, Wenjie Liu, Liantao Wang, Nan Front Plant Sci Plant Science The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root. Frontiers Media S.A. 2023-02-17 /pmc/articles/PMC9982017/ /pubmed/36875594 http://dx.doi.org/10.3389/fpls.2023.1122833 Text en Copyright © 2023 Yu, Tang, Zhu, Zhang, Liu and Wang 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
Yu, Qiushi
Tang, Hui
Zhu, Lingxiao
Zhang, Wenjie
Liu, Liantao
Wang, Nan
A method of cotton root segmentation based on edge devices
title A method of cotton root segmentation based on edge devices
title_full A method of cotton root segmentation based on edge devices
title_fullStr A method of cotton root segmentation based on edge devices
title_full_unstemmed A method of cotton root segmentation based on edge devices
title_short A method of cotton root segmentation based on edge devices
title_sort method of cotton root segmentation based on edge devices
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982017/
https://www.ncbi.nlm.nih.gov/pubmed/36875594
http://dx.doi.org/10.3389/fpls.2023.1122833
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