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.
Copyright Owner
Wiley Periodicals, Inc.
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Bao, Yin; Tang, Lie; Breitzman, Matthew W.; Salas-Fernandez, Maria G.; and Schnable, Patrick S., "Field-based Robot Phenotyping of Sorghum Plant Architecture using Stereo Vision" (2018). Agricultural and Biosystems Engineering Publications. 964.
https://lib.dr.iastate.edu/abe_eng_pubs/964
Included in
Agriculture Commons, Agronomy and Crop Sciences Commons, Bioresource and Agricultural Engineering Commons, Robotics Commons
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.