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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.01550

Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data

 Tyr Wiesner-Hanks1*, Harvey Wu2, Ethan Stewart3, Chad DeChant2, Nicholas Kaczmar3, Hod Lipson2,  Michael A. Gore3 and  Rebecca J. Nelson3
  • 1PepsiCo, United Kingdom
  • 2Columbia University, United States
  • 3Cornell University, United States

Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe and implement a two-step method for generating a large amount of high-quality training data with minimal expert input. First, experts located symptoms of northern leaf blight (NLB) in field images taken by unmanned aerial vehicles (UAVs), annotating them quickly at low resolution. Second, non-experts were asked to draw polygons around the identified diseased areas, producing high-resolution ground truths that were automatically screened based on agreement between multiple workers. We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy of 0.9979 and F1 score of 0.7153. The CNN trained on crowdsourced data showed greatly improved spatial resolution compared to one trained on expert-generated data, despite using only one fifth as many expert annotations. The final model was able to accurately delineate lesions down to the millimeter level from UAV-collected images, the finest scale of aerial plant disease detection achieved to date. The two-step approach to generating training data is a promising method to streamline deep learning approaches for plant disease detection, and for complex plant phenotyping tasks in general.

Keywords: phenotyping, UAV, plant disease, deep learning, machine learning, crowdsourcing

Received: 29 Jun 2019; Accepted: 06 Nov 2019.

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.

* Correspondence: Mx. Tyr Wiesner-Hanks, PepsiCo, Leicester, United Kingdom,