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High-Throughput Robotic Phenotyping of Energy Sorghum Crops

Conference paper
First Online:
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 5)

Abstract

Plant phenotyping is a time consuming, labour intensive, error prone process of measuring the physical properties of plants. We present a scalable robotic system which employs computer vision and machine learning to phenotype plants rapidly. It maintains high throughput making multiple phenotyping measurements during the plant lifecycle in plots containing thousands of plants. Our novel approach allows scanning of plants inside the plant canopy in addition to the top and bottom section of the plants. Here we present our design decisions, implementation challenges and field observations.

Keywords

Error-prone Process Sensor Pod Boom Sensor Leaf Angle Measured Phenotypic Estimates 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

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