Skip to content

Latest commit

 

History

History
117 lines (68 loc) · 7.16 KB

README.md

File metadata and controls

117 lines (68 loc) · 7.16 KB
Raw

CI logo

Template Instructions

Welcome,

This is the Code Institute student template for the Cherry Leaves project option in Predictive Analytics. We have preinstalled all of the tools you need to get started. It's perfectly okay to use this template as the basis for your project submissions. Click the Use this template button above to get started.

You can safely delete the Template Instructions section of this README.md file and modify the remaining paragraphs for your own project. Please do read the Template Instructions at least once, though! It contains some important information about the IDE and the extensions we use.

How to use this repo

  1. Use this template to create your GitHub project repo

  2. Log into your cloud IDE with your GitHub account.

  3. On your Dashboard, click on the New Workspace button

  4. Paste in the URL you copied from GitHub earlier

  5. Click Create

  6. Wait for the workspace to open. This can take a few minutes.

  7. Open a new terminal and pip3 install -r requirements.txt

  8. Open the jupyter_notebooks directory, and click on the notebook you want to open.

  9. Click the kernel button and choose Python Environments.

Note that the kernel says Python 3.8.18 as it inherits from the workspace, so it will be Python-3.8.18 as installed by our template. To confirm this, you can use ! python --version in a notebook code cell.

Cloud IDE Reminders

To log into the Heroku toolbelt CLI:

  1. Log in to your Heroku account and go to Account Settings in the menu under your avatar.
  2. Scroll down to the API Key and click Reveal
  3. Copy the key
  4. In the terminal, run heroku_config
  5. Paste in your API key when asked

You can now use the heroku CLI program - try running heroku apps to confirm it works. This API key is unique and private to you, so do not share it. If you accidentally make it public, then you can create a new one with Regenerate API Key.

Dataset Content

  • The dataset is sourced from Kaggle. We then created a fictitious user story where predictive analytics can be applied in a real project in the workplace.
  • The dataset contains +4 thousand images taken from the client's crop fields. The images show healthy cherry leaves and cherry leaves that have powdery mildew, a fungal disease that affects many plant species. The cherry plantation crop is one of the finest products in their portfolio, and the company is concerned about supplying the market with a compromised quality product.

Business Requirements

The cherry plantation crop from Farmy & Foods is facing a challenge where their cherry plantations have been presenting powdery mildew. Currently, the process is manual verification if a given cherry tree contains powdery mildew. An employee spends around 30 minutes in each tree, taking a few samples of tree leaves and verifying visually if the leaf tree is healthy or has powdery mildew. If there is powdery mildew, the employee applies a specific compound to kill the fungus. The time spent applying this compound is 1 minute. The company has thousands of cherry trees located on multiple farms across the country. As a result, this manual process is not scalable due to the time spent in the manual process inspection.

To save time in this process, the IT team suggested an ML system that detects instantly, using a leaf tree image, if it is healthy or has powdery mildew. A similar manual process is in place for other crops for detecting pests, and if this initiative is successful, there is a realistic chance to replicate this project for all other crops. The dataset is a collection of cherry leaf images provided by Farmy & Foods, taken from their crops.

  • 1 - The client is interested in conducting a study to visually differentiate a healthy cherry leaf from one with powdery mildew.
  • 2 - The client is interested in predicting if a cherry leaf is healthy or contains powdery mildew.

Hypothesis and how to validate?

  • List here your project hypothesis(es) and how you envision validating it (them).

The rationale to map the business requirements to the Data Visualisations and ML tasks

  • List your business requirements and a rationale to map them to the Data Visualisations and ML tasks.

ML Business Case

  • In the previous bullet, you potentially visualised an ML task to answer a business requirement. You should frame the business case using the method we covered in the course.

Dashboard Design

  • List all dashboard pages and their content, either blocks of information or widgets, like buttons, checkboxes, images, or any other items, that your dashboard library supports.
  • Finally, during the project development, you may revisit your dashboard plan to update a given feature (for example, at the beginning of the project, you were confident you would use a given plot to display an insight, but later, you chose another plot type).

Unfixed Bugs

  • You will need to mention unfixed bugs and why they were unfixed. This section should include shortcomings of the frameworks or technologies used. Although time can be a significant variable for consideration, paucity of time and difficulty understanding implementation is not a valid reason to leave bugs unfixed.

Deployment

Heroku

  • The App live link is: https://YOUR_APP_NAME.herokuapp.com/
  • Set the runtime.txt Python version to a Heroku-20 stack currently supported version.
  • The project was deployed to Heroku using the following steps.
  1. Log in to Heroku and create an App
  2. At the Deploy tab, select GitHub as the deployment method.
  3. Select your repository name and click Search. Once it is found, click Connect.
  4. Select the branch you want to deploy, then click Deploy Branch.
  5. The deployment process should happen smoothly if all deployment files are fully functional. Click the button Open App on the top of the page to access your App.
  6. If the slug size is too large, then add large files not required for the app to the .slugignore file.

Main Data Analysis and Machine Learning Libraries

  • Here, you should list the libraries used in the project and provide an example(s) of how you used these libraries.

Credits

  • In this section, you need to reference where you got your content, media and from where you got extra help. It is common practice to use code from other repositories and tutorials. However, it is necessary to be very specific about these sources to avoid plagiarism.
  • You can break the credits section up into Content and Media, depending on what you have included in your project.

Content

  • The text for the Home page was taken from Wikipedia Article A.
  • Instructions on how to implement form validation on the Sign-Up page were taken from Specific YouTube Tutorial.
  • The icons in the footer were taken from Font Awesome.

Media

  • The photos used on the home and sign-up page are from This Open-Source site.
  • The images used for the gallery page were taken from this other open-source site.

Acknowledgements (optional)

  • Thank the people who provided support throughout this project.