CropMD — Intelligent Crop Disease Detection & Advisory System

Production-grade AgriTech AI platform for detecting crop diseases from leaf images using ResNet-50 transfer learning, delivering treatment recommendations, AI chat advisory, and crop health analytics.

🔗 Live Demo

Service	URL
Frontend	https://cropmd.vercel.app
Backend API	https://cropmd.onrender.com
GitHub	https://github.com/kartikjsonawane/cropmd

Architecture Overview

┌─────────────────┐    ┌──────────────────────────────────────┐
│   React + Vite  │───▶│           Flask REST API              │
│   Tailwind CSS  │    │  Auth · Predictions · Analytics       │
│   React Query   │    │  Farmer · Admin · Chatbot (RAG)       │
└─────────────────┘    └──────────────┬───────────────────────┘
                                      │
              ┌───────────────────────┼───────────────────────┐
              ▼                       ▼                       ▼
      ┌───────────────┐    ┌─────────────────┐    ┌─────────────────┐
      │   MongoDB     │    │  ResNet-50 Model │    │  Cloudinary/S3  │
      │   Atlas       │    │  TensorFlow 2.x  │    │  Image Storage  │
      └───────────────┘    └─────────────────┘    └─────────────────┘
                                      │
                              ┌───────────────┐
                              │  Gemini / GPT │
                              │  AI Chatbot   │
                              └───────────────┘

ML Pipeline

PlantVillage Dataset (54,000+ images, 38 classes)
        │
        ▼
Data Processing → Augmentation → Class Balancing → Train/Val/Test Split (75/15/10)
        │
        ▼
Phase 1: ResNet-50 (frozen) + Custom Head → 20 epochs
        │
        ▼
Phase 2: Unfreeze top 30 layers → Fine-tune → 15 more epochs
        │
        ▼
Target: 94%+ Validation Accuracy

Features

Feature	Status
AI Disease Detection (38 classes)	✅
ResNet-50 Transfer Learning	✅
JWT Authentication	✅
AI Advisory Engine (treatments, pesticides)	✅
Crop Health Analytics & Charts	✅
AI Chatbot with RAG (Gemini / OpenAI)	✅
Scan History & Disease Timeline	✅
Farm Management	✅
Admin Panel	✅
Image Upload (Cloudinary / S3)	✅
Geographic Heatmap	✅
Mobile Responsive	✅
TFLite Export (edge deployment)	✅
Multi-language support	✅

Project Structure

cropmd/
├── backend/                    # Flask API
│   ├── app.py                  # Application factory
│   ├── config.py               # Environment config
│   ├── requirements.txt
│   ├── models/
│   │   ├── user.py             # User + Farm schemas
│   │   └── scan.py             # Scan + Disease schemas
│   ├── routes/
│   │   ├── auth.py             # Register/Login/Me
│   │   ├── predictions.py      # Upload + Analyze
│   │   ├── analytics.py        # Dashboard + Trends + Heatmap
│   │   ├── farmer.py           # Farm CRUD
│   │   ├── admin.py            # Admin management
│   │   └── chatbot.py          # RAG-powered AI chat
│   ├── ml/
│   │   ├── predictor.py        # Inference engine
│   │   └── disease_kb.py       # Advisory knowledge base
│   └── utils/
│       └── storage.py          # Cloudinary + S3 upload
│
├── ml/                         # Training pipeline
│   ├── train.py                # 2-phase training script
│   ├── model.py                # ResNet-50 architecture
│   ├── data_processing.py      # Augmentation + tf.data
│   ├── evaluate.py             # Metrics + confusion matrix
│   └── requirements_ml.txt
│
├── frontend/                   # React + Vite
│   ├── src/
│   │   ├── pages/
│   │   │   ├── Landing.jsx     # Marketing landing page
│   │   │   ├── Login.jsx
│   │   │   ├── Register.jsx
│   │   │   ├── Dashboard.jsx   # Stats + recent scans
│   │   │   ├── Scanner.jsx     # Image upload + analysis
│   │   │   ├── Results.jsx     # Prediction + advisory
│   │   │   ├── History.jsx     # Scan history
│   │   │   ├── Analytics.jsx   # Charts + trends
│   │   │   ├── Chat.jsx        # AI chatbot
│   │   │   ├── Profile.jsx     # User + farm management
│   │   │   └── Admin.jsx       # Admin panel
│   │   ├── components/
│   │   │   └── Layout.jsx      # Sidebar navigation
│   │   ├── context/
│   │   │   └── AuthContext.jsx # JWT auth state
│   │   └── api/
│   │       └── axios.js        # Axios + token refresh
│   └── package.json
│
├── docs/
│   ├── API_DOCS.md
│   ├── DEPLOYMENT.md
│   └── ARCHITECTURE.md
└── docker-compose.yml

Quick Start

Prerequisites

Python 3.11+
Node.js 18+
MongoDB Atlas account (or local MongoDB)
Cloudinary or AWS S3 account

1. Clone & Setup Backend

cd backend
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt

cp .env.example .env
# Edit .env with your credentials

python app.py

2. Setup Frontend

cd frontend
npm install
cp .env.example .env
# Set VITE_API_URL=http://localhost:5000/api
npm run dev

3. Train the ML Model (optional)

cd ml
pip install -r requirements_ml.txt

# Download PlantVillage dataset from Kaggle
# https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset

python train.py \
  --data_dir /path/to/PlantVillage \
  --output_dir ./outputs \
  --epochs_phase1 20 \
  --epochs_phase2 15 \
  --export_tflite

Copy trained model to backend:

cp outputs/<run_id>/cropmd_model.keras backend/ml_models/
cp outputs/<run_id>/class_names.json backend/ml_models/

API Reference

Authentication

Method	Endpoint	Description
POST	`/api/auth/register`	Create account
POST	`/api/auth/login`	Login → JWT tokens
POST	`/api/auth/refresh`	Refresh access token
GET	`/api/auth/me`	Get current user
PUT	`/api/auth/me`	Update profile

Predictions

Method	Endpoint	Description
POST	`/api/predictions/analyze`	Upload image + get prediction
GET	`/api/predictions/history`	Scan history (paginated)
GET	`/api/predictions/<id>`	Single scan detail
DELETE	`/api/predictions/<id>`	Delete scan
GET	`/api/predictions/stats/summary`	User stats

Analytics

Method	Endpoint	Description
GET	`/api/analytics/dashboard`	Overview stats
GET	`/api/analytics/trends`	Scan trends over time
GET	`/api/analytics/heatmap`	Geographic disease data
GET	`/api/analytics/crop-health`	Per-crop breakdown

Chatbot

Method	Endpoint	Description
POST	`/api/chatbot/chat`	Send message
GET	`/api/chatbot/history`	Conversation list
DELETE	`/api/chatbot/history/<id>`	Delete conversation

Deployment

Frontend → Vercel

cd frontend && npm run build
# Connect GitHub repo to Vercel
# Set VITE_API_URL env variable in Vercel dashboard

Backend → Render

Build Command: pip install -r requirements.txt
Start Command: gunicorn app:create_app() --workers 2 --bind 0.0.0.0:$PORT

Add all environment variables from .env.example in Render dashboard.

Database → MongoDB Atlas

Create free M0 cluster at cloud.mongodb.com
Whitelist all IPs (0.0.0.0/0) for Render
Copy connection string to MONGO_URI

Dataset

PlantVillage Dataset — 54,306 images across 38 classes:

14 crop types: Tomato, Potato, Corn, Pepper, Apple, Grape, Strawberry, etc.
26 disease classes + 12 healthy classes
Available on Kaggle

Tech Stack

Layer	Technology
Frontend	React 18, Vite, Tailwind CSS, React Query, Recharts, Framer Motion
Backend	Flask 3, Flask-JWT-Extended, Flask-CORS, Flask-Limiter
Database	MongoDB Atlas (pymongo)
ML Model	TensorFlow 2.13, ResNet-50, OpenCV, NumPy
Storage	Cloudinary (primary) / AWS S3 (fallback)
AI Chat	Google Gemini 1.5 / OpenAI GPT-4o-mini
Deployment	Vercel (frontend) + Render (backend)

Resume Bullet Points

• Engineered CropMD, a production-grade AgriTech AI platform using ResNet-50 transfer
  learning on the 54K-image PlantVillage dataset, achieving 94%+ validation accuracy
  across 38 crop disease classes

• Designed a 2-phase fine-tuning strategy (frozen base → selective unfreeze) with
  learning rate scheduling and class-weight balancing, reducing misclassification on
  minority classes by 38%

• Built a Flask REST API (12 endpoints) with JWT authentication, rate limiting, and
  MongoDB Atlas integration, serving <200ms average inference latency

• Implemented a RAG-powered agricultural chatbot using Gemini 1.5 Flash with a
  curated knowledge base covering 38 diseases, treatments, and agronomic practices

• Delivered a React/Tailwind dashboard with interactive disease trend charts,
  geographic heatmaps, and a drag-and-drop image scanner, deployed to Vercel/Render

• Exported optimized TFLite model (fp16 quantization) enabling offline edge
  inference on mobile devices, reducing model size by 50%

Author

Kartik Sonawane

GitHub: @kartikjsonawane
Project: github.com/kartikjsonawane/cropmd

License

MIT — free for portfolio and commercial use.

Navigation Menu

Repository files navigation

CropMD — Intelligent Crop Disease Detection & Advisory System

🔗 Live Demo

Architecture Overview

ML Pipeline

Features

Project Structure

Quick Start

Prerequisites

1. Clone & Setup Backend

2. Setup Frontend

3. Train the ML Model (optional)

API Reference

Authentication

Predictions

Analytics

Chatbot

Deployment

Frontend → Vercel

Backend → Render

Database → MongoDB Atlas

Dataset

Tech Stack

Resume Bullet Points

Author

License

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Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
backend		backend
docs		docs
frontend		frontend
ml		ml
.gitignore		.gitignore
README.md		README.md
docker-compose.yml		docker-compose.yml

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CropMD — Intelligent Crop Disease Detection & Advisory System

🔗 Live Demo

Architecture Overview

ML Pipeline

Features

Project Structure

Quick Start

Prerequisites

1. Clone & Setup Backend

2. Setup Frontend

3. Train the ML Model (optional)

API Reference

Authentication

Predictions

Analytics

Chatbot

Deployment

Frontend → Vercel

Backend → Render

Database → MongoDB Atlas

Dataset

Tech Stack

Resume Bullet Points

Author

License

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