Portfolio
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Khetibaadi
#flask #react #node-js #postgresql #docker #keras #pytorch #opencv
A GIS-based farm planning platform built to help farmers make data-driven decisions — crop recommendation, yield prediction, fertilizer guidance, and plant disease detection, wrapped in a multilingual web app with an NLP chatbot for onboarding.
Architecture
A Flask-based ML microservice sits behind the main Node.js/React app, exposing a RESTful API that the frontend calls for crop prediction, geo-location lookups, and chatbot responses — keeping the model-serving layer independent of the main application.
Models
- Crop yield prediction
- Fertilizer recommendation
- Plant disease detection — a ResNet CNN reaching 98% accuracy
- NLP-powered chatbot for guiding users through the app
Infrastructure
- PostgreSQL for data persistence
- Docker containers for backend deployment
Stack
- Frontend: React
- Backend: Node.js, Flask (ML microservice)
- ML/DL: Keras, PyTorch, OpenCV
- Data: PostgreSQL
- Infra: Docker