TY - JOUR

T1 - The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

AU - Thapa, Ranjita

AU - Zhang, Kai

AU - Snavely, Noah

AU - Belongie, Serge

AU - Khan, Awais

N1 - Funding Information: Financial support was received from the Cornell Initiative for Digital Agriculture (CIDA). The authors thank Zach Guillian (summer intern at Cornell AgriTech, Geneva, New York, USA) for help with data collection. Publisher Copyright: © 2020 Thapa et al. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America

PY - 2020/9/1

Y1 - 2020/9/1

N2 - Premise: Apple orchards in the United States 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 and increased environmental and health impacts. Methods and Results: We have manually captured 3651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert-annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine-Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off-the-shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held-out test set. Discussion: This data set will contribute toward 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 data set for a larger, more comprehensive expert-annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.

AB - Premise: Apple orchards in the United States 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 and increased environmental and health impacts. Methods and Results: We have manually captured 3651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert-annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine-Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off-the-shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held-out test set. Discussion: This data set will contribute toward 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 data set for a larger, more comprehensive expert-annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.

KW - apple orchards

KW - computer vision

KW - convolutional neural network

KW - disease classification

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85091611121&partnerID=8YFLogxK

U2 - 10.1002/aps3.11390

DO - 10.1002/aps3.11390

M3 - Journal article

AN - SCOPUS:85091611121

VL - 8

JO - Applications in Plant Sciences

JF - Applications in Plant Sciences

SN - 2168-0450

IS - 9

M1 - e11390

ER -