A Ground Point Fitting Method for Winter Wheat Height Estimation Using UAV-Based SfM Point Cloud Data

Xiaozhe Zhou; Minfeng Xing; Binbin He; Jinfei Wang; Yang Song; Jiali Shang; Chunhua Liao; Min Xu; Xiliang Ni

doi:10.3390/drones7070406

Drones (Jun 2023)

A Ground Point Fitting Method for Winter Wheat Height Estimation Using UAV-Based SfM Point Cloud Data

Xiaozhe Zhou,
Minfeng Xing,
Binbin He,
Jinfei Wang,
Yang Song,
Jiali Shang,
Chunhua Liao,
Min Xu,
Xiliang Ni

Affiliations

Xiaozhe Zhou: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Minfeng Xing: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Binbin He: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Jinfei Wang: Department of Geography, University of Western Ontario, London, ON N6A 5C2, Canada
Yang Song: Intelligent Agriculture Research Institute, Zoomlion Smart Agriculture, Changsha 410013, China
Jiali Shang: Agriculture and Agri-Food Canada, Ottawa, ON K1A0C6, Canada
Chunhua Liao: School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China
Min Xu: The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xiliang Ni: Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China

DOI: https://doi.org/10.3390/drones7070406
Journal volume & issue: Vol. 7, no. 7
p. 406

Abstract

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Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV images can quickly estimate crop height in the target area at a lower cost. However, crop leaves gradually start to cover the ground from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. The terrain undulations and outliers will seriously affect the height estimation accuracy. This paper proposed a ground point fitting method to estimate the height of winter wheat based on the UAV SfM point cloud. A canopy slice filter was designed to reduce the interference of middle canopy points and outliers. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the valid filtered point cloud. Then, the missing ground points were fitted according to the known ground points. Furthermore, we achieved crop height monitoring at the stem elongation stage with an R2 of 0.90. The relative root mean squared error (RRMSE) of height estimation was 5.9%, and the relative mean absolute error (RMAE) was 4.6% at the stem elongation stage. This paper proposed the canopy slice filter and fitting missing ground points. It was concluded that the canopy slice filter successfully optimized the extraction of ground points and removed outliers. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy.

Published in Drones

ISSN: 2504-446X (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Motor vehicles. Aeronautics. Astronautics
Website: http://www.mdpi.com/journal/drones

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