Computer Vision For Crop Disease Detection

An accurate image recognition model that can detect wheat rust from any image.

About Client

The CGIAR (formerly the Consultative Group for International Agricultural Research) is a global partnership engaged in research for a food-secured future. The CGIAR is made up of 15 research centers and operates in dozens of countries across Asia, Africa, and Latin America.

Client Goals

The goal of this project is to help farmers ease the process and time of identifying wheat rust in their crops. The client aims to expose the fascinating progress and unsolved problems of computational agriculture to the AI research community.

Business Challenge

Wheat rust is a devastating plant disease that affects many African crops, reducing yields and affecting the livelihoods of farmers and decreasing food security across the continent. The disease is difficult to monitor at a large scale, making it difficult to control and eradicate.


We built an end –end computer vision model from the data provided using cutting edge deep learning techniques.


The development team used React.js to build a frontend for the web service then it was hosted with Google Firebase service. This completed our task of building a full end-to-end computer vision-driven prediction service to detect wheat rust using images of wheat plants to determine healthy or rusty wheat.


Value Delivered

This project represents a potential breakthrough in our ability to monitor and control plant diseases like wheat rust that affect African livelihoods. The model we built can also be replicated on other crops grown especially in Africa such as maize, sorghum and millet, used with other plants and can be combined with UAV’s, drone technology to provide real-time imaging/prediction of large farmlands that have the potential of advancing agriculture.

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What do you want to build?

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