Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data.

ABSTRACT: 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 the

SUBMITTER: Wiesner-Hanks T 

PROVIDER: S-EPMC6927297 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature