Campus Units

Agronomy, Computer Science, Genetics, Development and Cell Biology, Mechanical Engineering, Psychology, Statistics, Veterinary Microbiology and Preventive Medicine, Bioinformatics and Computational Biology

Document Type

Article

Publication Version

Published Version

Publication Date

7-30-2018

Journal or Book Title

PloS ONE

Volume

14

Issue

7

First Page

e1006337

DOI

10.1371/journal.pcbi.1006337

Abstract

The accuracy of machine learning tasks critically depends 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 of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate 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, but with no significant difference between the two MTurk worker types. Furthermore, 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.

Comments

This article is published as Zhou, Naihui, Zachary D. Siegel, Scott Zarecor, Nigel Lee, Darwin A. Campbell, Carson M. Andorf, Dan Nettleton et al. "Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning." PLoS computational biology 14, no. 7 (2018): e1006337. doi: 10.1371/journal.pcbi.1006337.

Rights

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Language

en

File Format

application/pdf

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