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Prediction of bacterial pathogens of yellowing disease in cocconuts using ML

Identification and Prediction of Bacterial Pathogens Colonizing Yellowing Disease in Coastal Kenyan Coconuts: A Machine Learning Approach

Table of Contents

What are we doing? (And why?)

Problem

The lack of sufficient and open scientific data on Kenyan coconut diseases demands a comprehensive documentation effort. Identifying the bacterial pathogens behind these diseases is essential for effective disease management.

Solution

  • We propose to harness the power of machine learning models to achieve accurate detection and prediction of coconut yellowing diseases along Kenya's coastline.
  • Submission of generated genomic data to public databases

Unique Value Proposition

Our project stands out by combining advanced DNA analysis, machine learning techniques, and indigenous knowledge. This unique blend allows us to tackle the challenges posed by coconut yellowing diseases effectively.

Objectives

  • Submit genomic data to a public data base
  • Build a cat boost model to predict coconut yellowing disease (CYD) based on the bacterial diversity

Data

The data for this study is 16SrRNA dataset from yellowing diseased coconut plants from the Kenyan coast

KeyMetrics

  • Availing our data to a public data base
  • Accuracy of machine learning models

TargetUserProfiles

  • Agricultural researchers
  • Government agencies
  • Coconut farmers
  • Kenya Coconut Development Authority (KCDA)

ResourcesRequired

  • Bioinformatics tools
  • Machine learning frameworks (Python libraries)
  • Contributors from diverse backgrounds

ContributorProfilesandCommunicationChannels

Our project welcomes contributions from various fields, including:

  • Biologists
  • Data scientists
  • Bioinformaticians
  • Universities and Research Institutions
  • Passionate about coconut farming

How can you get involved ?

You can find us on: GitHub

Roadmap

To track our progress and upcoming tasks, check our Project Roadmap.

How to Contribute

  1. Clone the repository.
  2. Create a new branch: git checkout -b feature/your-feature-name.
  3. Commit your changes: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin feature/your-feature-name.
  5. Submit a pull request.

Code of Conduct

Please review and adhere to our Code of Conduct to ensure a positive and inclusive community.

License

This project is licensed under the MIT License. See the LICENSE.md file for details.

Team

  1. Fatma Omar
  2. Elisee Jafsia
  3. Umar Ahmad
  4. Adolf Oyesigye Mukama