🧑‍🌾 Smart Inventory Management System

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📌 Overview

The Smart Inventory Management System is an AI-powered solution designed for farmers and warehouses to track, analyze, and manage agricultural products in real-time.

It leverages YOLO (You Only Look Once) object detection, MongoDB for inventory storage, and Streamlit for visualization. Real-time alerts are sent via Twilio SMS/WhatsApp when stock is low or products are near expiry.

💡 Features include:

• 👁️ Automated Visual Tracking: Real-time object detection and counting using a live camera feed and YOLOv11.
• 📱 SMS Notifications: Instant Twilio-powered alerts when stock levels hit minimum thresholds.
• 📊 Interactive Dashboard: Clean React.js UI to view current stock, item status (OK/LOW), and update history.
• ⚙️ Flexible Management: Easily add new items, set initial stock, and define custom thresholds.
• ✏️ Manual Stock Control: Allows for manual adjustments and overrides to ensure data accuracy.

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📽️ Visual Preview

Dashboard Interface

Real-time Object Detection

SMS Alert System

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🏗️ System Architecture

graph TD
    A[Camera Feed] --> B(OpenCV Video Capture)
    B --> C{YOLOv11 Object Detection}
    C -- Object Crossed Line --> D[Update Database]
    D --> E{Stock < Threshold?}
    E -- Yes --> F[Trigger Twilio SMS API]
    F --> G[Farmer Receives Alert]
    D --> H[React Frontend]
    H -- REST API --> I[Flask Backend]
    I -- CRUD Operations --> J[( Database)]

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📑 Why YOLO?

YOLO (You Only Look Once) is chosen because:

• ✅ Real-time object detection suitable for farms/warehouses
• ✅ High accuracy for small + large object tracking
• ✅ Efficient for edge devices (Raspberry Pi/ESP32-CAM)

Reference Papers:

• YOLOv5: Real-Time Object Detection
• Applications of Deep Learning in Agriculture

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📂 Dataset & Sources

• Augmented using Roboflow for better YOLO training

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🛠️ Tech Stack

• Python 3.9+
• YOLOv5/YOLOv8 (for detection)
• OpenCV (image preprocessing & camera feed)
• MongoDB (NoSQL database for inventory)
• Streamlit (UI Dashboard)
• Twilio API (alerts & notifications)

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⚙️ Installation & Setup

1. Clone Repository

git clone https://github.com/your-username/smart-inventory.git
cd smart-inventory

2. Setup Virtual Environment & Install Dependencies

python -m venv venv
source venv/bin/activate   # Mac/Linux  
venv\Scripts\activate    # Windows  

pip install -r requirements.txt

3. Start Application

streamlit run app.py
npm start

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📊 Results

• ✅ 92% detection accuracy on crop dataset
• ✅ Real-time inventory tracking with MongoDB backend
• ✅ Automatic alerts & insights improve efficiency

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🔮 Future Scope

• 🚀 Integration with AR for farmer training & visualization
• 🌍 Multilingual chatbot for crop/fertilizer recommendations
• 📦 AI-powered supply chain optimization
• 🤖 AI Agents for autonomous decision-making

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📜 License

This project is licensed under the MIT License – see the LICENSE file for details.

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