Intervening Wheat Leaf Disease Using Computer Vision

This Omdena Local Chapter Challenge runs for 7 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Addis Ababa, Ethiopia Chapter.

The problem

Ethiopia’s high frequency of wheat leaf disease has raised serious concerns and poses a serious danger to farmers’ livelihoods and the country’s agricultural industry. wheat crops are suffering from the disease, which causes withering leaves, reduced development, and lower yields. Farmers now struggle with lower revenues, greater financial strain, and a higher danger of food insecurity.

Farmers’ farming techniques and Ethiopia’s general agricultural environment are in jeopardy due to the absence of efficient disease-fighting tactics, which has left them disappointed and demoralized. An immediate response is required to solve this issue and provide farmers with cutting-edge technology tools that can lessen the effects of wheat leaf disease, guarantee crop output, and safeguard the farming industry’s long-term survival.

The goals

The primary objective of our project is to develop and implement a computer vision-based solution to intervene and combat wheat leaf disease in Ethiopia. The goal is to empower farmers by providing them with an innovative tool that can accurately and efficiently detect the presence of the disease in wheat crops. By leveraging computer vision technology, we aim to enable early disease identification, thereby facilitating timely intervention measures and minimizing the negative impact on crop yield and quality.

Specifically, our project aims to achieve the following:

• Disease Detection: Develop a robust computer vision algorithm capable of accurately identifying wheat leaf disease symptoms, such as discoloration, lesions, and patterns indicative of specific diseases. The algorithm will analyze images of plant leaves captured by farmers using smartphones or other low-cost devices.
• Real-Time Diagnosis: Enable real-time diagnosis by integrating the computer vision algorithm with a user-friendly mobile application or web platform. This will allow farmers to capture images of affected plant leaves, which will be processed instantly to provide prompt disease diagnosis and relevant recommendations.
• Advisory System: Implement an advisory system that delivers personalized recommendations to farmers based on the diagnosed disease and the specific conditions of their crops. The system will provide guidance on suitable treatments, preventive measures, and best agricultural practices to effectively manage and mitigate the impact of wheat leaf disease.
• Farmer Empowerment: Conduct training programs and workshops to educate farmers about the computer vision solution, its functionality, and its potential benefits. Empower farmers with the necessary knowledge and skills to utilize the technology effectively, interpret results, and make informed decisions regarding disease management.
• Scalability and Accessibility: Ensure that the computer vision solution is accessible to a wide range of farmers, including those in remote areas with limited internet connectivity. Design the system to be lightweight, cost-effective, and compatible with various devices to facilitate widespread adoption and usage.

By accomplishing these objectives, our project aims to revolutionize the way wheat leaf disease is detected and managed in Ethiopia. We envision a future where farmers can proactively address the disease, reduce crop losses, optimize resource utilization, and ultimately enhance their agricultural productivity and economic well-being.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work