VitaliSee

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Inspiration

One of the greatest challenges faced to modern sustainability efforts is deforestation and de-vegetation. Not just in cities but also in rural areas where plant diseases greatly damage rural efforts to modernize and, especially in third world countries, overcome poverty. Inspired by the urgent need to address the challenges faced by farmers in detecting and managing crop diseases, our project leverages the power of machine learning to analyze images of plants. Recognizing the detrimental impact of undetected diseases on crop yield and sustainability, our solution combines efficiency and accessibility. The platform swiftly identifies diseases through image recognition and empowers farmers with a real-time dashboard, offering insights for effective protection, while also providing real-time tracking of plant growth for informed agricultural management. By fostering collective efforts to protect crops, we aim to promote sustainable agricultural practices. Our mission is to cultivate a resilient and informed farming community, ensuring healthier crops and fostering sustainability in agriculture. In addition, these features allow people from small towns to large cities to better take care of their plants to ensure future environmental sustainability in cities.

What it does

Our platform is a comprehensive solution designed to enhance plant health management through the power of machine learning (ML). It employs advanced image analysis techniques to detect diseases in crops and plants with high accuracy, allowing farmers and city-dwellers alike to take proactive measures for disease prevention and control. Some of the features include:

Image-based disease detection, which will tell whether the plant is infected or not
Real-time analytics and insights derived from the ML analysis, offering a clear overview of the plant's health status
An alert system(utilizing Twilio) notifies farmers in the vicinity about prevalent diseases, fostering collaborative efforts for disease control
Monitor overall plant growth, which will enable better decision-making regarding crop management practices
Optimal plant watering reminders and tracking to ensure plant health

How we built it

Front-end: React.js, CSS
Back-end: Flask(using Python), Firebase
ML model: Tensorflow
Twilio: SMS notifications

Challenges we ran into

Coming up with an innovative and feasible idea
Working with backend using Firebase, training the ML model, and implementing Twilio
Integrating the backend with the ML model and front end

Accomplishments that we're proud of

Collaborating together as a team (even though we were slightly different time zones)
Completing the project within the given time frame
Being able to implement most of the technical features

What we learned

Efficient time management and collaboration
Prioritization
Problem-solving abilities
Prototyping and creating an MVP
Importance of delegating tasks early and making sure that everyone understands exactly what needs to be done and what the mission and vision of the project is. Doing so allows for smooth operations and efficient workflow.

What's next for VitaliSee

Adding more new features (we had limited time, so we couldn't implement all the features we wanted to)
Integrate AI
Building a mobile app
Iterate and improve the features we already have