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

TL;DR

This paper introduces a large, curated image repository of healthy and infected crop leaves to facilitate the development of mobile disease diagnostics using machine learning, aiming to reduce yield losses globally.

Contribution

It provides a publicly accessible, extensive dataset of plant images and a platform to support crowdsourcing and machine learning for crop disease detection.

Findings

Over 50,000 curated plant images released

Platform enables crowdsourcing for disease diagnosis

Supports development of mobile-based diagnostic tools

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.