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Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature
High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the applic...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028843/ https://www.ncbi.nlm.nih.gov/pubmed/33851136 http://dx.doi.org/10.34133/2021/9765952 |
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author | Wang, Jian Wu, Bizhi Kohnen, Markus V. Lin, Daqi Yang, Changcai Wang, Xiaowei Qiang, Ailing Liu, Wei Kang, Jianbin Li, Hua Shen, Jing Yao, Tianhao Su, Jun Li, Bangyu Gu, Lianfeng |
author_facet | Wang, Jian Wu, Bizhi Kohnen, Markus V. Lin, Daqi Yang, Changcai Wang, Xiaowei Qiang, Ailing Liu, Wei Kang, Jianbin Li, Hua Shen, Jing Yao, Tianhao Su, Jun Li, Bangyu Gu, Lianfeng |
author_sort | Wang, Jian |
collection | PubMed |
description | High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency. |
format | Online Article Text |
id | pubmed-8028843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-80288432021-04-12 Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature Wang, Jian Wu, Bizhi Kohnen, Markus V. Lin, Daqi Yang, Changcai Wang, Xiaowei Qiang, Ailing Liu, Wei Kang, Jianbin Li, Hua Shen, Jing Yao, Tianhao Su, Jun Li, Bangyu Gu, Lianfeng Plant Phenomics Research Article High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency. AAAS 2021-03-30 /pmc/articles/PMC8028843/ /pubmed/33851136 http://dx.doi.org/10.34133/2021/9765952 Text en Copyright © 2021 Jian Wang et al. https://creativecommons.org/licenses/by/4.0/Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0). |
spellingShingle | Research Article Wang, Jian Wu, Bizhi Kohnen, Markus V. Lin, Daqi Yang, Changcai Wang, Xiaowei Qiang, Ailing Liu, Wei Kang, Jianbin Li, Hua Shen, Jing Yao, Tianhao Su, Jun Li, Bangyu Gu, Lianfeng Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature |
title | Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature |
title_full | Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature |
title_fullStr | Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature |
title_full_unstemmed | Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature |
title_short | Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature |
title_sort | classification of rice yield using uav-based hyperspectral imagery and lodging feature |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028843/ https://www.ncbi.nlm.nih.gov/pubmed/33851136 http://dx.doi.org/10.34133/2021/9765952 |
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