Agricultural and Biosystems Engineering Publications
Campus Units
Agricultural and Biosystems Engineering, Plant Pathology and Microbiology, Human Computer Interaction, Plant Sciences Institute
Document Type
Article
Publication Version
Published Version
Publication Date
9-12-2017
Journal or Book Title
Sensors
Volume
17
Issue
9
First Page
2082
Research Focus Area(s)
Biological and Process Engineering and Technology
DOI
10.3390/s17092082
Abstract
Crop breeding plays an important role in modern agriculture, improving plant performance, and increasing yield. Identifying the genes that are responsible for beneficial traits greatly facilitates plant breeding efforts for increasing crop production. However, associating genes and their functions with agronomic traits requires researchers to observe, measure, record, and analyze phenotypes of large numbers of plants, a repetitive and error-prone job if performed manually. An automated seedling phenotyping system aimed at replacing manual measurement, reducing sampling time, and increasing the allowable work time is thus highly valuable. Toward this goal, we developed an automated corn seedling phenotyping platform based on a time-of-flight of light (ToF) camera and an industrial robot arm. A ToF camera is mounted on the end effector of the robot arm. The arm positions the ToF camera at different viewpoints for acquiring 3D point cloud data. A camera-to-arm transformation matrix was calculated using a hand-eye calibration procedure and applied to transfer different viewpoints into an arm-based coordinate frame. Point cloud data filters were developed to remove the noise in the background and in the merged seedling point clouds. A 3D-to-2D projection and an x-axis pixel density distribution method were used to segment the stem and leaves. Finally, separated leaves were fitted with 3D curves for morphological traits characterization. This platform was tested on a sample of 60 corn plants at their early growth stages with between two to five leaves. The error ratios of the stem height and leave length measurements are 13.7% and 13.1%, respectively, demonstrating the feasibility of this robotic system for automated corn seedling phenotyping.
Access
Open
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright Owner
The Authors
Copyright Date
2017
Language
en
File Format
application/pdf
Recommended Citation
Lu, Hang; Tang, Lie; Whitham, Steven A.; and Mei, Yu, "A Robotic Platform for Corn Seedling Morphological Traits Characterization" (2017). Agricultural and Biosystems Engineering Publications. 941.
https://lib.dr.iastate.edu/abe_eng_pubs/941
Included in
Agriculture Commons, Bioresource and Agricultural Engineering Commons, Plant Breeding and Genetics Commons, Plant Pathology Commons
Comments
This article is published as Lu, Hang, Lie Tang, Steven A. Whitham, and Yu Mei. "A Robotic Platform for Corn Seedling Morphological Traits Characterization." Sensors 17, no. 9 (2017): 2082. DOI: 10.3390/s17092082. Posted with permission.