# An open access repository of images on plant health to enable the development of mobile disease diagnostics

arXiv: https://arxiv.org/abs/1511.08060 · HTML: https://mlsanity.com/paper/1511.08060/

## Key facts

- Cited by 135 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2015.

## Built on by (most cited first, 30 of 135)

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- [PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation](https://mlsanity.com/paper/2409.04038/) (2024)
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- [Semantic Segmentation with Labeling Uncertainty and Class Imbalance](https://mlsanity.com/paper/2102.04566/) (2021)
- [Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild](https://mlsanity.com/paper/2408.14723/) (2024)
- [DMLR: Data-centric Machine Learning Research -- Past, Present and Future](https://mlsanity.com/paper/2311.13028/) (2023)
- [AgriGPT-VL: Agricultural Vision-Language Understanding Suite](https://mlsanity.com/paper/2510.04002/) (2025)
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- [Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection](https://mlsanity.com/paper/2106.07770/) (2021)
- [Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models](https://mlsanity.com/paper/2208.02707/) (2022)
- [Leaf Image-based Plant Disease Identification using Color and Texture Features](https://mlsanity.com/paper/2102.04515/) (2021)
- [Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach](https://mlsanity.com/paper/2210.09509/) (2022)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
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- [Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT](https://mlsanity.com/paper/2207.07919/) (2022)
- [Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods](https://mlsanity.com/paper/2105.07660/) (2021)

## Abstract (arXiv)

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.