An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing

By David P. Hughes, Marcel Salathé. arXiv.org, 2015

1,436 citations recorded by the source corpus.

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%. Infectious diseases of crops are not new and historic examples such as the Irish Potato Famine of 1845-49 demonstrate this. But what is new is the widespread distribution of smartphones among crop growers around the world with an expected 5 billion smartphones by 2020. This 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. Computer vision and machine learning have shown their potential to automatically classify images. To do this for plant diseases requires a training set that facilitates the development of the algorithms. Here we announce the release of >50,000 expertly curated images on healthy and infected leaves of crops plants through the existing platform www.PlantVillage.org. 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.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/3e2da7c1c7dfc7960d1515b61f32fdc55359eea7

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Citations in the corpus

Plant leaf disease classification using EfficientNet deep learning model (2021, methodology): “In this study, PlantVillage dataset is used containing 38 classes and 54,305 images of 14 different plant species in total, 12 of which are healthy, 26 of which are diseased (Hughes and Salathe, 2015).”
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition (2017, methodology): “However, the Plant Village Dataset contains only images of leaves that are previously cropped in the ﬁeld and captured by a camera in the laboratory.”
Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm (2018, positive): “tomato leaf images on PlantVilliage dataset [8].”
A review on the main challenges in automatic plant disease identification based on visible range images (2016, background): “Fortunately, some initiatives are already underway to remedy this situation, such as the database developed by Hughes and Salathe (2015), in which more than 50,000 images of healthy and diseased plants are being made available (https://www.plantvillage.org/).”
Better Self-training for Image Classification through Self-supervision (2021, methodology): “We use three benchmark datasets: Street View House Numbers (SVHN) [15], CIFAR-10 [11], and PlantVillage [8].”
LARGE: Latent-Based Regression through GAN Semantics (2021, methodology): “Leaf image experiments use the Plant-Village dataset [23].”
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, background): “The largest public dataset currently was developed by Hughes & Salathe (2015) called PlantVillage and contains over than 54 thousand images of leaves and 38 classes, among them, healthy and diseased leaves of different species.”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, methodology): “ Regarding the predominance and characteristics of the data sets most used in the investigated studies, we highlight the customized initiatives designed mainly for a research need and the PlantVillage[34] data set. Figure 5 shows the distribution of studies by data set and type of image capture envi”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “Smart Agriculture [140] Images on plant health 2015 550 Through the current web platform PlantVillage, this dataset made available over 50,000 highly curated photos of healthy and diseased leaves of crop plants.”
Plant diseases and pests detection based on deep learning: a review (2021, background): “PlantVillage-Dataset: 50,000 images of classified plant diseases of 14 crop varieties and 26 diseases [84] Detached leaves on a plain background https ://githu b.”
An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “The PlantVillage dataset is a compilation of crop photos with labels indicating the presence of various illnesses (Hughes and Salathé, 2015).”
Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, methodology): “Hughes and Salathe [76] utilized this developed model for the identiﬁcation of diseases in the images dataset.”
Using Deep Learning for Image-Based Plant Disease Detection (2016, methodology): “Here, we demonstrate the technical feasibility using a deep learning approach utilizing 54,306 images of 14 crop species with 26 diseases (or healthy) made openly available through the project PlantVillage (Hughes and Salathé, 2015).”
Disease detection on the leaves of the tomato plants by using deep learning (2017, methodology): “Two different deep learning network architectures AlexNet [9] and SqueezeNet [10] are trained and tested on the tomato images of the Plantvillage dataset.”