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      Using Deep Learning for Image-Based Plant Disease Detection


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          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.

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          Most cited references18

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          Deep Learning in Neural Networks: An Overview

          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            Plant disease: a threat to global food security.

            A vast number of plant pathogens from viroids of a few hundred nucleotides to higher plants cause diseases in our crops. Their effects range from mild symptoms to catastrophes in which large areas planted to food crops are destroyed. Catastrophic plant disease exacerbates the current deficit of food supply in which at least 800 million people are inadequately fed. Plant pathogens are difficult to control because their populations are variable in time, space, and genotype. Most insidiously, they evolve, often overcoming the resistance that may have been the hard-won achievement of the plant breeder. In order to combat the losses they cause, it is necessary to define the problem and seek remedies. At the biological level, the requirements are for the speedy and accurate identification of the causal organism, accurate estimates of the severity of disease and its effect on yield, and identification of its virulence mechanisms. Disease may then be minimized by the reduction of the pathogen's inoculum, inhibition of its virulence mechanisms, and promotion of genetic diversity in the crop. Conventional plant breeding for resistance has an important role to play that can now be facilitated by marker-assisted selection. There is also a role for transgenic modification with genes that confer resistance. At the political level, there is a need to acknowledge that plant diseases threaten our food supplies and to devote adequate resources to their control.
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              Machine Learning for High-Throughput Stress Phenotyping in Plants.

              Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

                Author and article information

                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                22 September 2016
                : 7
                : 1419
                [1] 1Digital Epidemiology Lab, EPFL Geneva, Switzerland
                [2] 2School of Life Sciences, EPFL Lausanne, Switzerland
                [3] 3School of Computer and Communication Sciences, EPFL Lausanne, Switzerland
                [4] 4Department of Entomology, College of Agricultural Sciences, Penn State University State College, PA, USA
                [5] 5Department of Biology, Eberly College of Sciences, Penn State University State College, PA, USA
                [6] 6Center for Infectious Disease Dynamics, Huck Institutes of Life Sciences, Penn State University State College, PA, USA
                Author notes

                Edited by: Ashraf El-kereamy, University of California, USA

                Reviewed by: Julia Christine Meitz-Hopkins, Stellenbosch University, South Africa; Alberto Testolin, University of Padua, Italy

                *Correspondence: Marcel Salathé marcel.salathe@ 123456epfl.ch

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Copyright © 2016 Mohanty, Hughes and Salathé.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 19 June 2016
                : 06 September 2016
                Page count
                Figures: 4, Tables: 1, Equations: 0, References: 35, Pages: 10, Words: 6804
                Plant Science

                Plant science & Botany
                crop diseases,machine learning,deep learning,digital epidemiology
                Plant science & Botany
                crop diseases, machine learning, deep learning, digital epidemiology


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