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Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

Zachary D Siegel, Naihui Zhou, Scott Zarecor, Nigel Lee, Darwin A Campbell, Carson M Andorf, Dan Nettleton, Carolyn J Lawrence-Dill, Baskar Ganapathysubramanian, View ORCID ProfileIddo Friedberg, Jonathan W Kelly
doi: https://doi.org/10.1101/265918
Zachary D Siegel
1Department of Psychology, Iowa State University
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Naihui Zhou
2Bioinformatics and Computational Biology Program, Iowa State University
9Department of Veterinary Microbiology and Preventive Medicine, Iowa State University
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Scott Zarecor
3Department of Genetics, Development and Cell Biology, Iowa State University
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Nigel Lee
7Department of Mechanical Engineering, Iowa State University
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Darwin A Campbell
3Department of Genetics, Development and Cell Biology, Iowa State University
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Carson M Andorf
4Agricultural Research Services, United States Department of Agriculture
5Department of Computer Science, Iowa State University
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Dan Nettleton
2Bioinformatics and Computational Biology Program, Iowa State University
6Department of Statistics, Iowa State University
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Carolyn J Lawrence-Dill
2Bioinformatics and Computational Biology Program, Iowa State University
3Department of Genetics, Development and Cell Biology, Iowa State University
8Department of Agronomy, Iowa State University
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Baskar Ganapathysubramanian
7Department of Mechanical Engineering, Iowa State University
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Iddo Friedberg
2Bioinformatics and Computational Biology Program, Iowa State University
9Department of Veterinary Microbiology and Preventive Medicine, Iowa State University
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  • ORCID record for Iddo Friedberg
Jonathan W Kelly
1Department of Psychology, Iowa State University
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Abstract

The accuracy of machine learning tasks is critically dependent on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data points of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We explore the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, with no significant difference between the two MTurk worker types. The quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.

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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 4.0 International license.
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Posted February 15, 2018.
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Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning
Zachary D Siegel, Naihui Zhou, Scott Zarecor, Nigel Lee, Darwin A Campbell, Carson M Andorf, Dan Nettleton, Carolyn J Lawrence-Dill, Baskar Ganapathysubramanian, Iddo Friedberg, Jonathan W Kelly
bioRxiv 265918; doi: https://doi.org/10.1101/265918
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Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning
Zachary D Siegel, Naihui Zhou, Scott Zarecor, Nigel Lee, Darwin A Campbell, Carson M Andorf, Dan Nettleton, Carolyn J Lawrence-Dill, Baskar Ganapathysubramanian, Iddo Friedberg, Jonathan W Kelly
bioRxiv 265918; doi: https://doi.org/10.1101/265918

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