Image set for deep learning: field images of maize annotated with disease symptoms

Wiesner-Hanks, Tyr; Stewart, Ethan L.; Kaczmar, Nicholas; DeChant, Chad; Wu, Harvey; Nelson, Rebecca J.; Lipson, Hod; Gore, Michael A.

Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data.

Data description
This data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.


  • thumnail for 13104_2018_Article_3548.pdf 13104_2018_Article_3548.pdf application/pdf 977 KB Download File

Also Published In

BMC Research Notes

More About This Work

Academic Units
Computer Science
Mechanical Engineering
Published Here
October 1, 2018


Plant disease, Maize, Corn, Disease, Phytopathology, Machine learning, Convolutional neural network, Images, Deep learning