ML-POWERED TOMATO CROP DISEASE IDENTIFICATION AND MONITORING

A full-stack web application for crop leaf disease detection using a trained YOLOv8 model.

Features

• Upload leaf images and get a canonical disease label from the trained model.
• Frontend dashboard for scan history, insights and results.
• Backend API for uploads, inference and persistence.

Tech stack

• Frontend: React 18 + TypeScript, Vite, Tailwind CSS, Radix UI (shadcn-style components).
• Backend: Node.js + TypeScript, Express, tsx for running TS directly.
• Database: PostgreSQL via Drizzle ORM and drizzle-kit migrations.
• Machine learning: Python (Ultralytics YOLOv8, PyTorch, ONNX Runtime optional).
• Dev tools: Vite, TypeScript, tsx, tailwindcss, cross-env.

Quickstart (development) Prerequisites

• Node 18+ and npm
• Python 3.10+ (for ML tooling)
• PostgreSQL (or a compatible DATABASE_URL)

1. Clone the repo

git clone  && cd MP

2. Install Node dependencies

npm install

3. Configure environment

• Create a .env at the project root with at least:

DATABASE_URL=postgres://user:pass@host:port/dbname
SESSION_SECRET=your_session_secret
# Optional: OPENAI_API_KEY=...

4. (Optional) Prepare Python environment for ML

python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r ml/requirements.txt

5. Run database migrations

npm run db:push

6. Start the app in development

npm run dev

The dev script runs the TypeScript server (see server/index.ts) which also serves the client during development.

Production build

npm run build
npm start

Machine learning / inference

• The trained model expected by the server is at ml/models/best.pt (or the provided yolov8n.pt for experiments).
• To run a quick inference locally:

python ml/predict.py path/to/image.jpg

• To evaluate the dataset splits:

python ml/predict.py --eval test [limit]

Troubleshooting

• If you see “DATABASE_URL, ensure the database is provisioned” the .env or PostgreSQL connection is not configured correctly.
• Model errors will indicate missing ml/models/best.pt — ensure the file exists before running inference.

Where to look in the repo

• Server code: server/index.ts
• Frontend: client/src
• ML: ml (training, inference and requirements)