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Simulated Plant Images Improve Maize Leaf Counting Accuracy

View ORCID ProfileChenyong Miao, View ORCID ProfileThomas P. Hoban, View ORCID ProfileAlejandro Pages, View ORCID ProfileZheng Xu, Eric Rodene, View ORCID ProfileJordan Ubbens, Ian Stavness, View ORCID ProfileJinliang Yang, View ORCID ProfileJames C. Schnable
doi: https://doi.org/10.1101/706994
Chenyong Miao
1Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
3Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE, USA
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Thomas P. Hoban
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
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Alejandro Pages
4Department of Computer Science, University of Nebraska-Lincoln, Lincoln, USA
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Zheng Xu
5Department of Mathematics and Statistics, Wright State University, Dayton, OH, USA
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Eric Rodene
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
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Jordan Ubbens
6Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Ian Stavness
6Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Jinliang Yang
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
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James C. Schnable
1Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
3Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE, USA
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  • For correspondence: schnable@unl.edu
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ABSTRACT

Automatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.

Footnotes

  • https://www.doi.org/10.25739/f12d-tt60

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 18, 2019.
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Simulated Plant Images Improve Maize Leaf Counting Accuracy
Chenyong Miao, Thomas P. Hoban, Alejandro Pages, Zheng Xu, Eric Rodene, Jordan Ubbens, Ian Stavness, Jinliang Yang, James C. Schnable
bioRxiv 706994; doi: https://doi.org/10.1101/706994
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Simulated Plant Images Improve Maize Leaf Counting Accuracy
Chenyong Miao, Thomas P. Hoban, Alejandro Pages, Zheng Xu, Eric Rodene, Jordan Ubbens, Ian Stavness, Jinliang Yang, James C. Schnable
bioRxiv 706994; doi: https://doi.org/10.1101/706994

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