PDP (Plant Disease Prediction)

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Inspiration

Inspired by the challenges faced by farmers and the impact of plant diseases on agriculture, PDP aims to empower farmers with a user-friendly solution. Motivated by the potential positive impact on crop yield and livelihoods, our team integrates machine learning to predict plant diseases accurately.

What it does

PDP is a Plant Disease Prediction system designed to identify and predict diseases in plants using machine learning. Users can upload images of plants, and the system provides predictions regarding the presence of diseases.

How we built it

The project was built using a combination of machine learning and web development technologies. The disease prediction model was trained on a diverse dataset of plant images using Random Forest algorithm. The user interface was developed using html, css , javascript and bootstrap. The backend, responsible for handling predictions, was implemented using javascript.

Challenges we ran into

Making the model to train was a challenging task however with the teamwork we have got a success rate of 95-97% accuracy

Accomplishments that we're proud of

Successful implementation of a robust machine learning model for disease prediction. User-friendly interface design to enhance accessibility. Overcoming challenges and delivering a functional prototype within the given timeframe.

What we learned

Gain insights into the complexities of training machine learning models for plant disease prediction. Experience in integrating machine learning models with web interfaces. Collaboration and effective communication within a team setting.

What's next for PDP (Plant Disease Prediction)

Enhanced Model Accuracy: Continuously improve the machine learning model for higher accuracy in disease prediction. Expand Dataset: Incorporate a larger and more diverse dataset to improve the model's ability to identify various plant diseases. Real-time Monitoring: Explore the possibility of real-time disease monitoring in agricultural settings. Community Engagement: Encourage community contributions and feedback to enhance the project's functionality and scope.

Built With

• api
• bootstrap
• css
• googlecolab
• html
• javascript
• kaggle