TITLE: The Plant Pathology 2020 challenge dataset to classify foliar disease of apples 

ABSTRACT: Apple orchards in the U.S. are under constant threat from a large number of
pathogens and insects. Appropriate and timely deployment of disease management
depends on early disease detection. Incorrect and delayed diagnosis can result
in either excessive or inadequate use of chemicals, with increased production
costs, environmental, and health impacts. We have manually captured 3,651
high-quality, real-life symptom images of multiple apple foliar diseases, with
variable illumination, angles, surfaces, and noise. A subset, expert-annotated
to create a pilot dataset for apple scab, cedar apple rust, and healthy leaves,
was made available to the Kaggle community for 'Plant Pathology Challenge';
part of the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2020
(Computer Vision and Pattern Recognition). We also trained an off-the-shelf
convolutional neural network (CNN) on this data for disease classification and
achieved 97% accuracy on a held-out test set. This dataset will contribute
towards development and deployment of machine learning-based automated plant
disease classification algorithms to ultimately realize fast and accurate
disease detection. We will continue to add images to the pilot dataset for a
larger, more comprehensive expert-annotated dataset for future Kaggle
competitions and to explore more advanced methods for disease classification
and quantification.
Apple orchards in the U.S. are under constant threat from a large number of
pathogens and insects. Appropriate and timely deployment of disease management
depends on early disease detection. Incorrect and delayed diagnosis can result
in either excessive or inadequate use of chemicals, with increased production
costs, environmental, and health impacts. We have manually captured 3,651
high-quality, real-life symptom images of multiple apple foliar diseases, with
variable illumination, angles, surfaces, and noise. A subset, expert-annotated
to create a pilot dataset for apple scab, cedar apple rust, and healthy leaves,
was made available to the Kaggle community for 'Plant Pathology Challenge';
part of the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2020
(Computer Vision and Pattern Recognition). We also trained an off-the-shelf
convolutional neural network (CNN) on this data for disease classification and
achieved 97% accuracy on a held-out test set. This dataset will contribute
towards development and deployment of machine learning-based automated plant
disease classification algorithms to ultimately realize fast and accurate
disease detection. We will continue to add images to the pilot dataset for a
larger, more comprehensive expert-annotated dataset for future Kaggle
competitions and to explore more advanced methods for disease classification
and quantification.
The Plant Pathology 2020 challenge dataset to classify foliar disease of apples
http://arxiv.org/abs/2004.11958v1