PT  - JOURNAL ARTICLE
AU  - Zachary D Siegel
AU  - Naihui Zhou
AU  - Scott Zarecor
AU  - Nigel Lee
AU  - Darwin A Campbell
AU  - Carson M Andorf
AU  - Dan Nettleton
AU  - Carolyn J Lawrence-Dill
AU  - Baskar Ganapathysubramanian
AU  - Iddo Friedberg
AU  - Jonathan W Kelly
TI  - Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning
AID  - 10.1101/265918
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 265918
4099  - http://biorxiv.org/content/early/2018/02/14/265918.short
4100  - http://biorxiv.org/content/early/2018/02/14/265918.full
AB  - 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.