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SKU: hard-322

IoT Based AI Smart Waste Classification & Segregation System

AI-powered waste classification system using TensorFlow & MobileNetV3 for real-time garbage sorting into Multiple categories. Features live camera feed integration, image upload, and instant classification results.

Rs 15000.00

40 In Stock
Tested & Quality Guaranteed Pre-tested & Quality Checked before dispatch across Pakistan. 100% Genuine Guaranteed.
Fast Local & Nationwide Shipping Same-Day Dispatch in Lahore | 2-3 Days COD to Karachi, Islamabad & All Pakistan.

AI Smart Waste Classification & Segregation System – Realtime Garbage Sorting with Django & TensorFlow

Welcome to the future of smart waste management! This AI-powered waste classification system automatically identifies and sorts garbage into Multiple categories in real-time using deep learning. The system integrates seamlessly with Django for a powerful web-based dashboard, offering both live camera feed (ESP32-CAM/DroidCam) and image upload capabilities for instant classification.

Built with TensorFlow MobileNetV3, this system achieves high-precision classification, making it perfect for smart cities, recycling automation, and environmental sustainability initiatives.

🔍 How the System Works
📸 Live Camera Feed: Connects to ESP32-CAM or DroidCam for real-time video streaming and instant classification.

🧠 Deep Learning Model: TensorFlow/Keras MobileNetV3 trained on a comprehensive waste dataset.

🔄 Realtime Classification: Classifies waste into 6 categories instantly with confidence score display.

📊 Web Dashboard: Django-based interface showing live predictions, category labels, and probability percentages.

📱 Multi-Input Support: Classify waste via live camera stream OR upload images directly.

Quick Build & Interfacing Guide:

Step 1: Hardware Inspection

Unbox and verify all pre-tested sensors, microcontrollers, and wiring components included in your package.

Step 2: Chassis & Sensor Mount

Secure mechanical brackets, servos, and modules to the main frame using the provided mounting hardware.

Step 3: Pinout Wiring

Interface VCC, GND, and GPIO signal lines according to the verified schematic diagram provided with the kit.

Step 4: Code Upload & Testing

Upload the tested source code via Arduino IDE, Python, or ESP-IDF and verify live sensor readings and actuation.

Prototyping Kit Note: All DIY project kits come with complete verified code, connection diagrams, and 7-day technical replacement support across Pakistan.

Key Features

DroidCam Integration Use smartphone as IP camera
ESP32-CAM Support Connect camera for live streaming
MobileNetV3 Deep Learning Model Pre-trained TensorFlow/Keras model for high-accuracy waste classification
Mutiple Waste Categories Classifies into Cardboard, Glass, Metal, Paper, Plastic, and Trash
Real-time Predictions Instant classification from live camera feed

Specifications

Backend Django (Python)
Database SQL LITE
Deployment Dockerized container (manual installation requires extra setup cost of 5000)
Frontend HTML, CSS, JavaScript, Bootstrap
Note If You Need AnyKind of Code Snippet Related To This Project We Will Provide
Ram/Processor 8gb/3.5Ghz

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