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Article

An Efficient Method for Estimating Wheat Heading Dates Using UAV Images

1
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Science, The University of Tokyo, Tokyo 188-0002, Japan
3
Institute of Cotton Research, Shanxi Agricultural University, Yuncheng 044000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Shawn C. Kefauver
Remote Sens. 2021, 13(16), 3067; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163067
Received: 21 May 2021 / Revised: 24 July 2021 / Accepted: 30 July 2021 / Published: 4 August 2021
Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observational equipment, which is expensive. Therefore, in this study, we proposed a growth curve-based method for estimating wheat heading dates. The method first generates a height-based continuous growth curve based on five time-series unmanned aerial vehicle (UAV) images captured over the entire wheat growth cycle (>200 d). Then estimate the heading date by generated growth curve. As a result, the proposed method had a mean absolute error of 2.81 d and a root mean square error of 3.49 d for 72 wheat plots composed of different varieties and densities sown on different dates. Thus, the proposed method is straightforward, efficient, and affordable and meets the high-throughput estimation requirements of large-scale fields and underdeveloped areas. View Full-Text
Keywords: heading date; UAV images; plant height; growth curve; wheat heading date; UAV images; plant height; growth curve; wheat
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    Doi: 10.5281/zenodo.4777563
    Link: https://0-doi-org.brum.beds.ac.uk/10.5281/zenodo.4777563
    Description: Folder S1: growth curve of nine functions, Folder S2: wheat coverage growth curves, Folder S3: wheat plant height growth curves and second derivatives of sampling methods 1, 2, and 3, Folder S4: wheat plant height growth curve by four-time data, Figure S1: value correction of Digital Surface Model, Table S1: Plot designs for field experiments, Table S2: Wheat plant height estimated by full region sampling, Table S3: Wheat plant height estimated by random sampling, Table S4: Wheat plant height estimated of sample 1 by equal interval sampling, Table S5: Wheat plant height estimated of sample 2 by equal interval sampling, Table S6: Wheat plant height estimated of sample 3 by equal interval sampling, Table S7: Wheat plant height estimated of sample 4 by equal interval sampling, Table S8: Wheat plant height estimated of sample 5 by equal interval sampling, Table S9: Heading dates obtained by experts and estimated by sampling methods 1, 2, and 3, Table S10: Estimated heading date of equal interval sampling.
MDPI and ACS Style

Zhao, L.; Guo, W.; Wang, J.; Wang, H.; Duan, Y.; Wang, C.; Wu, W.; Shi, Y. An Efficient Method for Estimating Wheat Heading Dates Using UAV Images. Remote Sens. 2021, 13, 3067. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163067

AMA Style

Zhao L, Guo W, Wang J, Wang H, Duan Y, Wang C, Wu W, Shi Y. An Efficient Method for Estimating Wheat Heading Dates Using UAV Images. Remote Sensing. 2021; 13(16):3067. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163067

Chicago/Turabian Style

Zhao, Licheng, Wei Guo, Jian Wang, Haozhou Wang, Yulin Duan, Cong Wang, Wenbin Wu, and Yun Shi. 2021. "An Efficient Method for Estimating Wheat Heading Dates Using UAV Images" Remote Sensing 13, no. 16: 3067. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163067

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