Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering, Agronomy, Human Computer Interaction, Plant Sciences Institute

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

10-27-2018

Journal or Book Title

Journal of Field Robotics

Research Focus Area(s)

Advanced Machinery Engineering and Manufacturing Systems, Biological and Process Engineering and Technology

DOI

10.1002/rob.21830

Abstract

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops.

Comments

This is the peer-reviewed version of the following article: Bao, Yin, Lie Tang, Matthew W. Breitzman, Maria G. Salas Fernandez, and Patrick S. Schnable. "Field‐based robotic phenotyping of sorghum plant architecture using stereo vision." Journal of Field Robotics (2018), which has been published in final form at DOI: 10.1002/rob.21830. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Posted with permission.

Copyright Owner

Wiley Periodicals, Inc.

Language

en

File Format

application/pdf

Available for download on Sunday, October 27, 2019

Published Version

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