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. 2019 Dec 12;10:1550. doi: 10.3389/fpls.2019.01550

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Copyright © 2019 Wiesner-Hanks, Wu, Stewart, DeChant, Kaczmar, Lipson, Gore and Nelson

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Original image (top row), ground truth annotations drawn by Amazon Mechanical Turk workers (middle row), and conditional random field (CRF) segmentation (bottom row) for all seven test images in which CRF segmentation and ground truth diverged highly. In left five images, crowdsourced-polygon model outperformed humans by identifying lesionated areas where human experts had missed them. In the right two images, the model falsely classified senescent leaf tissue as lesions. White = lesion, black = non-lesion.