High-throughput Robotic Phenotyping of Energy Sorghum Crops
Conference Paper, Proceedings of 11th International Conference on Field and Service Robotics (FSR '17), pp. 99 - 113, September, 2017
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.
BibTeX
@conference{Vijayarangan-2017-27274,author = {Srinivasan Vijayarangan and Paloma Sodhi and Prathamesh Kini and James Bourne and Simon Du and Hanqi Sun and Barnabas Poczos and Dimitrios (Dimi) Apostolopoulos and David Wettergreen},
title = {High-throughput Robotic Phenotyping of Energy Sorghum Crops},
booktitle = {Proceedings of 11th International Conference on Field and Service Robotics (FSR '17)},
year = {2017},
month = {September},
pages = {99 - 113},
keywords = {Plant Phenotyping, Computer Vision, Multi-view Reconstruction, Field Robot Design, Machine Learning},
}
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