Plant Bay

💡 Inspiration

Often we have plants that we don't know how to take care of and they got neglected. Resulting in getting infected and dying. We want to help people to take care of their plants by providing them with information about the disease and its cure. We also have a weather page that shows the weather of the user's location. This will help the user to know the weather of their location and take care of their plants accordingly.

💻 What it does

Our web app "Plant Bay" is built with features:

• Upload a picture of the plant and get the information about the disease.
• Know the steps to cure the disease.
• Get the weather of the user's location.

⚙️ How we built it

• ML: Microsoft Cloud(Azure)
• Frontend: React Js
• Backend: Node Js
• Styling: Tailwind CSS

☁ Best Use of Microsoft Cloud for Social Impact

I am using Microsoft Azure to train my ML model. I am using Azure Notebooks to train my model. I am using Azure Blob Storage to store my dataset. I am using Azure Cognitive Services to get the image from the user. Microsoft Azure made it very easy to train my model and deploy it.

☘ Best Sustainability Hack sponsored by Avanade

The application is really helpful for farmers and people who are interested in gardening. It can help them to identify plant diseases and also the best way to cure them. It will help people to save their plants and also save the environment.

🤝 Most Creative Use of GitHub

We are using GitHub for the following reasons:

• Setting up the project: GitHub makes it easy to set up a project and get started.
• GitHub Project: For planning and keeping track of our project and its progress using GitHub projects.
• Using PRs and Issues: We are doing multiple PRs and building multiple issues to keep on track of the project.

🧠 Challenges we ran into

• Completing the project was a challenge because we had to implement the project by a deadline.
• It was my first time using Azure and I had to learn a lot of new things.
• It was a bit difficult to train the model because of the limited dataset.
• It was difficult to get the image from the user and then process it.

🏅 Accomplishments that we're proud of

• We are proud of the fact that we were able to complete the project in the given time.
• Using Azure for the first time and learning a lot of new things.
• We are proud of the fact that we were able to train the model and deploy it.

📖 What we learned

• Using Azure Cognitive Services to get the image from the user and train it.
• Implementing the ML model using Azure Notebooks.

🚀 What's next for Plant Bay

• Building a mobile application.
• Creating my ML model.
• Adding more features.