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

[Fatma366]

Project Lead: Fatma366

Mentor: Jafsia Umar

Welcome to OLS-8! This issue will be used to track your project and progress during the program. Please use this checklist over the next few weeks as you start Open Life Science program :tada:.

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Week 1: Meet your mentor!

• [x] Meet mentor for 30 minutes
• [x] Create an account on GitHub
• [x] Check if you have access to the HackMD notes set up for your meetings with your mentor
• [x] Prepare to meet your mentor(s) by completing a short homework provided in your shared notes
• [x] Complete your own copy of the open leadership self-assessment and share it to your mentor If you're a group, each teammate should complete this assessment individually. This is here to help you set your own personal goals during the program. No need to share your results, but be ready to share your thoughts with your mentor.
• [x] Make sure you know when and how you'll be meeting with your mentor.

Before Week 2: Cohort Call (Welcome to Open Life Science!)

• [x] Attend call or catch up via YouTube

• [x] Create an issue on the OLS-4 GitHub repository for your OLS work and share the link to your mentor.

• [x] Draft a brief vision statement using your goals

  This lesson from the Open Leadership Training Series (OLTS) might be helpful

• [x] Leave a comment on this issue with your draft vision statement & be ready to share this on the call

• [x] Check the Syllabus for notes and connection info for all the cohort calls.

Before Week 3: Meet your mentor!

• [x] Meet mentor
• [x] Look up two other projects and comment on their issues with feedback on their vision statement
• [x] Complete this compare and contrast assignment about current and desired community interactions and value exchanges
• [x] Complete your Open Canvas (instructions, canvas)
• [x] Share a link to your Open Canvas in your GitHub issue
• [x] Start your Roadmap
• [x] Comment on your issue with your draft Roadmap
• [x] Suggest a cohort name at the bottom of the shared notes and vote on your favorite with a +1

Before Week 4: Cohort Call (Tooling and roadmapping for Open projects)

• [x] Attend call or catch up via YouTube
• [x] Look up two other projects and comment on their issues with feedback on their open canvas.

Week 5 and later

• [x] Meet mentor
• [x] Create a GitHub repository for your project
• [x] Add the link to your repository in your issue
• [x] Use your canvas to start writing a README.md file, or landing page, for your project
• [x] Link to your README in a comment on this issue
• [x] Add an open license to your repository as a file called LICENSE.md
• [x] Add a Code of Conduct to your repository as a file called CODE_OF_CONDUCT.md
• [x] Invite new contributors to into your work!

This issue is here to help you keep track of work as you start Open Life Science program. Please refer to the OLS-4 Syllabus for more detailed weekly notes and assignments past week 4.

Week 6

• [x] Attend call or catch up via YouTube

Week 7

• [x] Meet mentor

Week 8

• [x] Attend call or catch up via YouTube

Week 9

• [x] Meet mentor

Week 10

• [x] Attend call or catch up via YouTube

Week 11

• [x] Meet mentor

Week 12

• [x] Attend call or catch up via YouTube

Week 13

• [x] Meet mentor

Week 14

• [ ] Attend call or catch up via YouTube

Week 15

• [ ] Meet mentor

[Fatma366]

Vision statement We are trying to leverage the power of machine learning models to revolutionize the detection and classification of genetic variants in bacteria colonizing yellowing diseases in coconut palms along the Kenyan coast. We aim to achieve this by applying different machine learning models (XgBoost, LightGBM, and Random Forest). We believe that this project has the potential to thrive as an open-source initiative, hosted on GitHub, where the community can actively participate. This involvement could take the form of opening new issues to request additional features or proposing innovative ideas. Furthermore, experienced users may choose to contribute by submitting their own code changes through pull requests to enhance the project further. We hope that through fostering collaboration and knowledge-sharing will all provide a dynamic research environment where diverse perspectives converge to harness the full potential of machine learning in agricultural pathology.

[Monsurat-Onabajo]

Hi Fatma, This is a really amazing project and after reading your vision statement, I am wondering how this project will benefit the farmers

[Fatma366]

Identifying these pathogens is the first step to coming up with mitigation strategies for this diseases

[Monsurat-Onabajo]

Ok, so it is going to be like a way of reducing the occurrence of these diseases in plants in order to have optimal farm yield and increase profit?

[Fatma366]

Our primary goal is to advance the detection and classification of genetic variants in coconut palm diseases using machine learning, which, in the long run, can contribute to more effective disease management and improved agricultural yields

[Monsurat-Onabajo]

Ok, I understand now, Thank you @Fatma366

[Fatma366]

Our Open Canvas

[Fatma366]

Identification-and-Prediction-using-ML-Roadmap

[Fatma366]

Here's the link for the repository of our project: Identification-and-Prediction-using-ML

[Fatma366]

README.md

[Antonis-Koutsoumpis]

Your project seems very ambitious and interesting! Hope the best! While reading the vision statement, which is very nicely written, I thought that you can further highlight the problems that this project solves as well as the practical or theoretical contributions. But everything looks super intersting, best of luck!

[YHordijk]

Our Open Canvas

Hi Fatma, your open canvas seems to be privated. Could you make it public, I would love to see it!

[Fatma366]

Our Open Canvas

Hi Fatma, your open canvas seems to be privated. Could you make it public, I would love to see it! Done, you can now access it!