A tool enabling farmers to upload images of diseased crops and receive immediate insights:
- 🌱 Machine Learning-based disease identification
- 📄 Auto-generated report with:
- Disease name
- Description
- Key Symptoms
- Causes
- Recommended organic and inorganic treatments
- Prevention methods
- 📊 Visual tracking of disease progression over time, using photos to estimate spread and trends
This helps farmers understand and manage crop health effectively and sustainably.
- Upload one or multiple images via a web or mobile interface
- Disease classification using a trained ML model
- Provide personalized treatment solution to farmers by generating local weather specific reports
- Rich report generation per upload which can be downloaded in PDF format
- Progress-tracking dashboard:
- Graphs of disease severity over time
- Aggregated statistics & trend analysis
- Backend: NodeJS, Flask(Python), RestAPI
- ML Model: Efficientnet, Groq client with Meta llama3-70b-8192 model
- Frontend: React Native with Expo
- Database: PostgreSQl
- Install prerequisites:
- Download and install the latest version of postgreSQL.
- install the nodeJS framework as well as python.
- Clone the github repo.
- Run the database.sql file to create the database.
- Set up the env file.
- In a terminal, change to the backend directory and run,
npm i node server
- In a new terminal, change to backend/ML_Models directory, and run,
python disease.py
- Similarly in another terminal, run,
python diseaseSpread.py
- Finally, in a fourth new terminal, change to the front_end directory and run,
npm i npx expo start
- Scan the QR code generated with the expo go app on your mobile phone, or plug in your phone to your PC and run it from there.
- Kartik D. Shinde - https://github.com/HalianCage
- Samiksha Surwase - https://github.com/samiksha0420
- Srushti Ghogare - https://github.com/srushtighogare
- Tanish Kulkarni - https://github.com/tanishkulkarni