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Open AccessArticle

A Robotic Platform for Corn Seedling Morphological Traits Characterization

1
Department of Agricultural and Biosystems Engineering, Iowa State University, 2346 Elings Hall, Ames, IA 50011, USA
2
Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(9), 2082; https://doi.org/10.3390/s17092082
Received: 16 July 2017 / Revised: 3 September 2017 / Accepted: 6 September 2017 / Published: 12 September 2017
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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. View Full-Text
Keywords: plant phenotyping; corn breeding; 3D reconstruction; point cloud; robot arm; ToF camera plant phenotyping; corn breeding; 3D reconstruction; point cloud; robot arm; ToF camera
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MDPI and ACS Style

Lu, H.; Tang, L.; Whitham, S.A.; Mei, Y. A Robotic Platform for Corn Seedling Morphological Traits Characterization. Sensors 2017, 17, 2082.

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