title: Using Deep Learning for Image-Based Plant Disease Detection.
abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
authors.affiliation: Digital Epidemiology Lab, EPFLGeneva, Switzerland; School of Life Sciences, EPFLLausanne, Switzerland; School of Computer and Communication Sciences, EPFLLausanne, Switzerland.
authors.forename: Sharada P
authors.initials: SP
authors.lastname: Mohanty
authors.affiliation: Department of Entomology, College of Agricultural Sciences, Penn State UniversityState College, PA, USA; Department of Biology, Eberly College of Sciences, Penn State UniversityState College, PA, USA; Center for Infectious Disease Dynamics, Huck Institutes of Life Sciences, Penn State UniversityState College, PA, USA.
authors.forename: David P
authors.initials: DP
authors.lastname: Hughes
authors.affiliation: Digital Epidemiology Lab, EPFLGeneva, Switzerland; School of Life Sciences, EPFLLausanne, Switzerland; School of Computer and Communication Sciences, EPFLLausanne, Switzerland.
authors.forename: Marcel
authors.initials: M
authors.lastname: Salathé
keywords: crop diseases; deep learning; digital epidemiology; machine learning