Inspiration

Early treatment and prevention leads to healthy plants. Healthy plants lead to healthy people, animals, and a healthy planet. We want to help our planet become healthier. Also, my grandparents were rural farmers who were not well informed of the best ways to take care of plants and prevent plant diseases.

What it does

Our simple and efficient instructions and tips help you and your fellow planters plan the best times and ways to water your plants so that they are healthy and robust. We also help diagnose any of your currently affected plants so you can receive an initial diagnosis needed for further treatment.

How we built it

HTML, CSS, REPLIT, GOOGLE FONTS, Python

Challenges we ran into

Syncing up different styles and formats for each HTML page into one CSS page can result in code errors. Increasing the accuracy of the CNN

Accomplishments that we're proud of

We finished this website in less than 5-6 hours.

What we learned

To work fast and come up with ideas fast.

What's next for Plan-T

To provide more tips for plant care and classify more types of diseases. Improve the accuracy of the CNN model

What is the purpose? To help planters plan the best times and ways to water your plants so that they are healthy and robust so animals and humans can get a healthy diet. We also help diagnose any of your currently affected plants so you can receive an initial diagnosis needed for further treatment using our PlantNet CNN trained to identify whether a plant is Healthy, Powdery, or Rusty. PlantNet diagnoses pictures of plants as Healthy, Rusty, or Powdery has an accuracy of 85%. This is useful for farmers who live in rural areas who may not have access to medical plant care.

How is it unique? Our idea of plant disease diagnosis into a web app is very unique. What did your team learn? To work and communicate fast. What are some challenges you faced and how did you address them? Syncing up different styles and formats for each HTML page into one CSS page can result in code errors. Therefore we changed the div and section class names for each page to reduce any code confusion. It was also difficult to preprocess the data for the Plan-T Neural Network.

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