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An open access repository of images on plant health to enable the development of mobile disease diagnostics
David P. Hughes,Marcel Salathé +1 more
TLDR
These data are the beginning of an on-going, crowdsourcing effort to enable computer vision approaches to help solve the problem of yield losses in crop plants due to infectious diseases.Abstract:
Human society needs to increase food production by an estimated 70% by 2050 to feed an expected population size that is predicted to be over 9 billion people. Currently, infectious diseases reduce the potential yield by an average of 40% with many farmers in the developing world experiencing yield losses as high as 100%. The widespread distribution of smartphones among crop growers around the world with an expected 5 billion smartphones by 2020 offers the potential of turning the smartphone into a valuable tool for diverse communities growing food. One potential application is the development of mobile disease diagnostics through machine learning and crowdsourcing. Here we announce the release of over 50,000 expertly curated images on healthy and infected leaves of crops plants through the existing online platform PlantVillage. We describe both the data and the platform. These data are the beginning of an on-going, crowdsourcing effort to enable computer vision approaches to help solve the problem of yield losses in crop plants due to infectious diseases.read more
Citations
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Journal ArticleDOI
Using Deep Learning for Image-Based Plant Disease Detection
TL;DR: In this article, a deep convolutional neural network was used to identify 14 crop species and 26 diseases (or absence thereof) using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions.
Journal ArticleDOI
Plant disease identification from individual lesions and spots using deep learning
TL;DR: The use of individual lesions and spots for the task, rather than considering the entire leaf, allows the identification of multiple diseases affecting the same leaf and indicates that, as long as enough data is available, deep learning techniques are effective for plant disease detection and recognition.
Journal ArticleDOI
Plant leaf disease classification using EfficientNet deep learning model
TL;DR: In this study, EfficientNet deep learning architecture was proposed in plant leaf disease classification and the performance of this model was compared with other state-of-the-art deep learning models.
Proceedings ArticleDOI
Disease detection on the leaves of the tomato plants by using deep learning
TL;DR: The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses by using deep learning to detect the various diseases on the leaves of tomato plants.
References
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