基于无人机影像的小麦植株密度估算方法研究

doi:10.6046/gtzyyg.2020.04.16

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小麦是密植型作物,每亩地的播种量近20 kg,冬小麦植株密度的高低会直接影响最终产量,因此,实时监测小麦植株密度是保证小麦产量的重要途径。目前,获取小麦植株密度的主要方式是以人工测量为主,费时、费力。本文利用大疆悟2无人机(unmanned aerial vehicle,UAV)搭载禅思X4S相机,分架次获取小麦种植区的高分辨率可见光影像,基于无人机影像提取小麦覆盖度,并建立小麦覆盖度与植株密度之间的关系,从而实现基于UAV影像的小麦植株密度的快速获取。实验表明: ①利用改进的HSI颜色模型提取小麦覆盖度比传统目视估测、人工计数等分类方法提高了提取精度及效率,克服了不同架次无人机影像的光照条件差异以及阴影的影响; ②苗期、越冬期和返青期小麦覆盖度与植株密度之间都具有较高的相关性,其中,基于无人机影像提取的覆盖度与小麦植株密度的相关系数R²在3个生育期分别为0.737 9,0.898 1和0.897 6。利用牛腾雨村样本对关系模型的验证结果显示,基于所建立的关系模型的反演结果与实测值之间也具有较好的相关性,R²达到了0.919 8。

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	王伟
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关键词 ：小麦, 无人机影像, 覆盖度, 植株密度, HSI颜色模型, Hough变换

Abstract：

Wheat is a densely planted crop, and the planting volume per acre is nearly 20 kg. The plant density of winter wheat will directly affect the final yield. Therefore, real-time monitoring of wheat plant density is an important way to ensure wheat yield. At present, the main method for obtaining the plant density of wheat is mainly manual measurement, which is time-consuming and laborious. In this paper, the DJ inspire 2 UAV is equipped with a Zens X4S camera to obtain high-resolution visible light images of wheat planting areas, extract wheat coverage based on UAV images, and establish the relationship between plant density and plant density so as to achieve rapid acquisition of wheat plant density based on UAV image. Experiments show the following results: ① Using the improved HSI color model to extract wheat coverage improves accuracy and extraction efficiency compared with traditional visual estimation, manual counting and other classification methods, and overcomes differences in lighting conditions and shadows of different sorts of UAV images influences. ② There is a high correlation between wheat coverage and plant density at the seedling stage, overwintering stage and turning green stage. Among them, the correlation coefficient R² between the coverage based on drone image and the plant density of wheat are 0.737 9, 0.898 1 and 0.897 6 in three growth stages. The verification results of the relationship model using Niutengyu Village samples show that the inversion results based on the established relationship model also have a good correlation with the measured values, and R² reaches 0.919 8.

Key words： wheat UAV image vegetation cover planting density HSI color model Hough transform

收稿日期: 2019-11-18 出版日期: 2020-12-23

TP79

基金资助:国家自然科学基金项目“联合时序遥感影像和地基激光雷达的玉米生长过程监测方法研究”(41671433);国家重点研发计划项目“粮食丰产增效科技创新”(2017YFD0300903)

通讯作者: 苏伟

作者简介: 王伟(1987-),男,硕士研究生,主要从事农业遥感应用研究。Email:wangwei007@cau.edu.cn。

引用本文:

王伟, 王新盛, 姚婵, 金添, 邬佳昱, 苏伟. 基于无人机影像的小麦植株密度估算方法研究[J]. 国土资源遥感, 2020, 32(4): 111-119.
WANG Wei, WANG Xinsheng, YAO Chan, JIN Tian, WU Jiayu, SU Wei. Estimation of wheat planting density using UAV image. Remote Sensing for Land & Resources, 2020, 32(4): 111-119.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.04.16 或 https://www.gtzyyg.com/CN/Y2020/V32/I4/111

Fig.1 研究区及无人机影像(局部)

Tab.1 无人机影像获取时间及飞行参数

Fig.2 地面样方实测

Fig.3 基于无人机影像的小麦植株密度估算及缺苗率建议流程

Fig.4 基于无人机影像结合地面实测数据的小麦植株密度估算过程

Fig.5 小麦覆盖度与地面实测苗数的相关关系

Fig.6 基于平均垄间距和基于无人机影像估算缺苗的小麦覆盖度与植株密度的相关关系

Fig.7 小麦植株密度提取精度验证结果

Fig.8 缺苗率的计算结果(局部)

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