Extraction Method of Maize Height Based on UAV Remote Sensing

被引：0

作者：

Zhang H. ^{[1
]}

Tan Z. ^{[1
]}

Han W. ^{[2
]}

Zhu S. ^{[1
]}

Zhang S. ^{[1
]}

Ge C. ^{[1
]}

机构：

[1] College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi

[2] College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, Shaanxi

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷 / 05期

关键词：

Digital orthophoto map; Digital surface model; Maize height; Remote sensing; UAV;

D O I：

10.6041/j.issn.1000-1298.2019.05.028

中图分类号：

学科分类号：

摘要：

In order to accurately and quickly grasp the growth information of maize in the growth cycle, different digital orthophoto maps(DOM)and digital surface model (DSM) in the four stages of the nutritional growth stage of maize were obtained by unmanned aerial vehicle(UAV). K-means, genetic neural network and skeleton algorithm were used to extract the maize areas in the DOM, generate masks, and combined with DSM sets to obtain the height information of maize. Compared with the field measurement of plant height, the R2 of three methods were 0.853, 0.877 and 0.923, respectively, RMSE were 15.886 cm, 14.519 cm and 11.493 cm, respectively, MAE were 13.743 cm, 11.884 cm and 8.927 cm, respectively. The results showed that combining DOM and DSM can better extract the height value of maize in the nutritional growth stage. Compared with K-means and genetic neural network, the maize height extracted by the skeleton algorithm was highly consistent with the field measurement (R2 was 0.923, RMSE was 11.493 cm, MAE was 8.927 cm), and the extraction accuracy was high. Skeleton extraction combining DOM and DSM provided a way to extract plant height, which can be used as a reference for monitoring maize height by UAV remote sensing. © 2019, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：241 / 250

页数：9

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