Ground truth evaluation of computer vision based 3D reconstruction of synthesized and real plant images

Ground truth evaluation of computer vision based 3D reconstruction of synthesized and real plant... There is an increasing interest in using 3D computer vision in precision agriculture. This calls for better quantitative evaluation and understanding of computer vision methods. This paper proposes a test framework using ray traced crop scenes that allows in-depth analysis of algorithm performance and finds the optimal hardware and light source setup before investing in expensive equipment and field experiments. It was expected to be a valuable tool to structure the otherwise incomprehensibly large information space and to see relationships between parameter configurations and crop features. Images of real plants with similar structural categories were annotated manually for comparison in order to validate the performance results on the synthesised images. The results showed substantial correlation between synthesized and real plants, but only when all error sources were accounted for in the simulation. However, there were exceptions where there were structural differences between the virtual plant and the real plant that were unaccounted for by its category. The test framework was evaluated to be a valuable tool to uncover information from complex data structures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Ground truth evaluation of computer vision based 3D reconstruction of synthesized and real plant images

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Publisher
Kluwer Academic Publishers-Plenum Publishers
Copyright
Copyright © 2007 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-006-9028-3
Publisher site
See Article on Publisher Site

Abstract

There is an increasing interest in using 3D computer vision in precision agriculture. This calls for better quantitative evaluation and understanding of computer vision methods. This paper proposes a test framework using ray traced crop scenes that allows in-depth analysis of algorithm performance and finds the optimal hardware and light source setup before investing in expensive equipment and field experiments. It was expected to be a valuable tool to structure the otherwise incomprehensibly large information space and to see relationships between parameter configurations and crop features. Images of real plants with similar structural categories were annotated manually for comparison in order to validate the performance results on the synthesised images. The results showed substantial correlation between synthesized and real plants, but only when all error sources were accounted for in the simulation. However, there were exceptions where there were structural differences between the virtual plant and the real plant that were unaccounted for by its category. The test framework was evaluated to be a valuable tool to uncover information from complex data structures.

Journal

Precision AgricultureSpringer Journals

Published: Jan 13, 2007

References

  • Robotic weed control system for tomatoes
    Lee, WS; Slaughter, DC; Giles, DK
  • Future directions in precision agriculture
    McBratney, A; Whelan, B; Ancev, T; Bouma, J

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