Wheat ear detection in plots by segmenting mobile laser scanner data

Kaaviya Velumani, S. Oude Elberink, M. Y. Yang, Frédéric Baret

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

16 Citations (Scopus)
3 Downloads (Pure)

Abstract

The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxel-based segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85 %, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94 % and takes less than 20 seconds to process 50,000 points with an average point density of 16  points/cm2. Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points.
Original languageEnglish
Title of host publicationProceedings ISPRS Geospatial Week 2017, 18-22 September 2017, Wuhan, China
EditorsD. Li
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages149-156
Number of pages8
DOIs
Publication statusPublished - 2017
EventISPRS Geospatial Week 2017 - Wuhan, China
Duration: 18 Sept 201722 Sept 2017
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/ (Full text Open Access proceedings)

Publication series

NameISPRS Annals
PublisherISPRS
VolumeIV-2/W4
ISSN (Print)2194-9050

Conference

ConferenceISPRS Geospatial Week 2017
Country/TerritoryChina
CityWuhan
Period18/09/1722/09/17
Internet address

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