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Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks

Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth st...

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Autores principales: Zhang, Jian, Zhao, Biquan, Yang, Chenghai, Shi, Yeyin, Liao, Qingxi, Zhou, Guangsheng, Wang, Chufeng, Xie, Tianjin, Jiang, Zhao, Zhang, Dongyan, Yang, Wanneng, Huang, Chenglong, Xie, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298076/
https://www.ncbi.nlm.nih.gov/pubmed/32587594
http://dx.doi.org/10.3389/fpls.2020.00617
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author Zhang, Jian
Zhao, Biquan
Yang, Chenghai
Shi, Yeyin
Liao, Qingxi
Zhou, Guangsheng
Wang, Chufeng
Xie, Tianjin
Jiang, Zhao
Zhang, Dongyan
Yang, Wanneng
Huang, Chenglong
Xie, Jing
author_facet Zhang, Jian
Zhao, Biquan
Yang, Chenghai
Shi, Yeyin
Liao, Qingxi
Zhou, Guangsheng
Wang, Chufeng
Xie, Tianjin
Jiang, Zhao
Zhang, Dongyan
Yang, Wanneng
Huang, Chenglong
Xie, Jing
author_sort Zhang, Jian
collection PubMed
description Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.
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spelling pubmed-72980762020-06-24 Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks Zhang, Jian Zhao, Biquan Yang, Chenghai Shi, Yeyin Liao, Qingxi Zhou, Guangsheng Wang, Chufeng Xie, Tianjin Jiang, Zhao Zhang, Dongyan Yang, Wanneng Huang, Chenglong Xie, Jing Front Plant Sci Plant Science Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato. Frontiers Media S.A. 2020-06-10 /pmc/articles/PMC7298076/ /pubmed/32587594 http://dx.doi.org/10.3389/fpls.2020.00617 Text en Copyright © 2020 Zhang, Zhao, Yang, Shi, Liao, Zhou, Wang, Xie, Jiang, Zhang, Yang, Huang and Xie. http://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, Jian
Zhao, Biquan
Yang, Chenghai
Shi, Yeyin
Liao, Qingxi
Zhou, Guangsheng
Wang, Chufeng
Xie, Tianjin
Jiang, Zhao
Zhang, Dongyan
Yang, Wanneng
Huang, Chenglong
Xie, Jing
Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks
title Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks
title_full Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks
title_fullStr Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks
title_full_unstemmed Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks
title_short Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks
title_sort rapeseed stand count estimation at leaf development stages with uav imagery and convolutional neural networks
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298076/
https://www.ncbi.nlm.nih.gov/pubmed/32587594
http://dx.doi.org/10.3389/fpls.2020.00617
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