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Crowdsourcing Image Analysis For Plant Phenomics To Generate Ground Truth Data For Machine Learning, Naihui Zhou, Zachary D. Siegel, Scott Zarecor, Nigel Lee, Darwin A. Campbell, Carson M. Andorf, Dan Nettleton, Carolyn J. Lawrence-Dill, Baskar Ganapathysubramanian, Jonathan W. Kelly, Iddo Friedberg

Dan Nettleton

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 …