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Wheat lodging extraction using Improved_Unet network

The accurate extraction of wheat lodging areas can provide important technical support for post-disaster yield loss assessment and lodging-resistant wheat breeding. At present, wheat lodging assessment is facing the contradiction between timeliness and accuracy, and there is also a lack of effective...

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Autores principales: Yu, Jun, Cheng, Tao, Cai, Ning, Lin, Fenfang, Zhou, Xin-Gen, Du, Shizhou, Zhang, Dongyan, Zhang, Gan, Liang, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563998/
https://www.ncbi.nlm.nih.gov/pubmed/36247550
http://dx.doi.org/10.3389/fpls.2022.1009835
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author Yu, Jun
Cheng, Tao
Cai, Ning
Lin, Fenfang
Zhou, Xin-Gen
Du, Shizhou
Zhang, Dongyan
Zhang, Gan
Liang, Dong
author_facet Yu, Jun
Cheng, Tao
Cai, Ning
Lin, Fenfang
Zhou, Xin-Gen
Du, Shizhou
Zhang, Dongyan
Zhang, Gan
Liang, Dong
author_sort Yu, Jun
collection PubMed
description The accurate extraction of wheat lodging areas can provide important technical support for post-disaster yield loss assessment and lodging-resistant wheat breeding. At present, wheat lodging assessment is facing the contradiction between timeliness and accuracy, and there is also a lack of effective lodging extraction methods. This study aims to propose a wheat lodging assessment method applicable to multiple Unmanned Aerial Vehicle (UAV) flight heights. The quadrotor UAV was used to collect high-definition images of wheat canopy at the grain filling and maturity stages, and the Unet network was evaluated and improved by introducing the Involution operator and Dense block module. The performance of the Improved_Unet was determined using the data collected from different flight heights, and the robustness of the improved network was verified with data from different years in two different geographical locations. The results of analyses show that (1) the Improved_Unet network was better than other networks (Segnet, Unet and DeeplabV3+ networks) evaluated in terms of segmentation accuracy, with the average improvement of each indicator being 3% and the maximum average improvement being 6%. The Improved_Unet network was more effective in extracting wheat lodging areas at the maturity stage. The four evaluation indicators, Precision, Dice, Recall, and Accuracy, were all the highest, which were 0.907, 0.929, 0.884, and 0.933, respectively; (2) the Improved_Unet network had the strongest robustness, and its Precision, Dice, Recall, and Accuracy reached 0.851, 0.892, 0.844, and 0.885, respectively, at the verification stage of using lodging data from other wheat production areas; and (3) the flight height had an influence on the lodging segmentation accuracy. The results of verification show that the 20-m flight height performed the best among the flight heights of 20, 40, 80 and 120 m evaluated, and the segmentation accuracy decreased with the increase of the flight height. The Precision, Dice, Recall, and Accuracy of the Improved_Unet changed from 0.907 to 0.845, from 0.929 to 0.864, from 0.884 to 0.841, and from 0.933 to 0.881, respectively. The results demonstrate the improved ability of the Improved-Unet to extract wheat lodging features. The proposed deep learning network can effectively extract the areas of wheat lodging, and the different height fusion models developed from this study can provide a more comprehensive reference for the automatic extraction of wheat lodging.
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spelling pubmed-95639982022-10-15 Wheat lodging extraction using Improved_Unet network Yu, Jun Cheng, Tao Cai, Ning Lin, Fenfang Zhou, Xin-Gen Du, Shizhou Zhang, Dongyan Zhang, Gan Liang, Dong Front Plant Sci Plant Science The accurate extraction of wheat lodging areas can provide important technical support for post-disaster yield loss assessment and lodging-resistant wheat breeding. At present, wheat lodging assessment is facing the contradiction between timeliness and accuracy, and there is also a lack of effective lodging extraction methods. This study aims to propose a wheat lodging assessment method applicable to multiple Unmanned Aerial Vehicle (UAV) flight heights. The quadrotor UAV was used to collect high-definition images of wheat canopy at the grain filling and maturity stages, and the Unet network was evaluated and improved by introducing the Involution operator and Dense block module. The performance of the Improved_Unet was determined using the data collected from different flight heights, and the robustness of the improved network was verified with data from different years in two different geographical locations. The results of analyses show that (1) the Improved_Unet network was better than other networks (Segnet, Unet and DeeplabV3+ networks) evaluated in terms of segmentation accuracy, with the average improvement of each indicator being 3% and the maximum average improvement being 6%. The Improved_Unet network was more effective in extracting wheat lodging areas at the maturity stage. The four evaluation indicators, Precision, Dice, Recall, and Accuracy, were all the highest, which were 0.907, 0.929, 0.884, and 0.933, respectively; (2) the Improved_Unet network had the strongest robustness, and its Precision, Dice, Recall, and Accuracy reached 0.851, 0.892, 0.844, and 0.885, respectively, at the verification stage of using lodging data from other wheat production areas; and (3) the flight height had an influence on the lodging segmentation accuracy. The results of verification show that the 20-m flight height performed the best among the flight heights of 20, 40, 80 and 120 m evaluated, and the segmentation accuracy decreased with the increase of the flight height. The Precision, Dice, Recall, and Accuracy of the Improved_Unet changed from 0.907 to 0.845, from 0.929 to 0.864, from 0.884 to 0.841, and from 0.933 to 0.881, respectively. The results demonstrate the improved ability of the Improved-Unet to extract wheat lodging features. The proposed deep learning network can effectively extract the areas of wheat lodging, and the different height fusion models developed from this study can provide a more comprehensive reference for the automatic extraction of wheat lodging. Frontiers Media S.A. 2022-09-30 /pmc/articles/PMC9563998/ /pubmed/36247550 http://dx.doi.org/10.3389/fpls.2022.1009835 Text en Copyright © 2022 Yu, Cheng, Cai, Lin, Zhou, Du, Zhang, Zhang and Liang 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, Jun
Cheng, Tao
Cai, Ning
Lin, Fenfang
Zhou, Xin-Gen
Du, Shizhou
Zhang, Dongyan
Zhang, Gan
Liang, Dong
Wheat lodging extraction using Improved_Unet network
title Wheat lodging extraction using Improved_Unet network
title_full Wheat lodging extraction using Improved_Unet network
title_fullStr Wheat lodging extraction using Improved_Unet network
title_full_unstemmed Wheat lodging extraction using Improved_Unet network
title_short Wheat lodging extraction using Improved_Unet network
title_sort wheat lodging extraction using improved_unet network
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563998/
https://www.ncbi.nlm.nih.gov/pubmed/36247550
http://dx.doi.org/10.3389/fpls.2022.1009835
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