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.

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.

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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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