An Efficient Method for Estimating Wheat Heading Dates Using UAV Images

文献类型： 外文期刊

第一作者： Zhao, Licheng

作者： Zhao, Licheng;Duan, Yulin;Wang, Cong;Wu, Wenbin;Shi, Yun;Zhao, Licheng;Guo, Wei;Wang, Haozhou;Wang, Jian

作者机构：

关键词： heading date; UAV images; plant height; growth curve; wheat

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2021 年 13 卷 16 期

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收录情况： SCI

摘要： 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.

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