IoT-Enabled Waste Sorting and Disposal Machine

Waste Sorting and Disposal Machine

We at HashStudioz designed a smart washing machine controller. It works by adding smart capabilities and home assistant connectivity such as Alex and Google Home to a washing machine. The status of reporting is similar to machine health and the current operation cycle. It is a developed and integrated ESP-32-based solution that handles the App communication and Smart home integration. It uses Application and Device gateway development: For smooth integration with hardware.

Waste Sorting and Disposal Machine - Problem Statement

Waste sorting mechanism:

Distinguish between Glass, Plastic and Tin.

Segregation of Materials:

Reporting whenever the bins are full.

Eliminate Redundant KYC process.

UI for inputting Mobile number and Trash Type.

Live Rewarding feature.

Waste Sorting and Disposal Machine - Solution

Android Based UI:

For Mobile Number and Trash Type.

Hardware Backed Trash Detection System.

Api developed with trash data being sent:

To allow data collection and reward distribution.

Multiple Sensors for Redundancy.

Optional ML connectivity.

Waste Sorting and Disposal Machine - System Architecture

Frequently Asked Questions

The Smart IoT Waste Sorting Machine is an automated disposal terminal that classifies and segregates recyclable waste materials (such as glass, plastic, and metal cans) using optical sensors, inductive proximity detectors, and edge AI computer vision before compacting and binning the items into dedicated compartments.

Ultrasonic and optical time-of-flight (ToF) sensors continuously monitor real-time fill percentages inside each segregated compartment. When bins reach threshold capacity (typically 80% to 90%), the onboard microcontroller dispatches instant automated alerts over Wi-Fi or 4G-LTE to municipal sanitation routing dashboards for prompt collection scheduling.

The system is powered by an industrial ESP32 / STM32 microcontroller architecture interfacing with motor drivers, weight load cells, and sensor buses over I2C and UART. It connects to cloud backends via MQTT/HTTPS and supports voice integration with smart assistants like Amazon Alexa and Google Home.

Users authenticate via mobile phone number or QR code scan on the integrated touchscreen display before depositing recyclables. Real-time verification APIs calculate the quantity and material weight of deposited items, instantly crediting cashback rewards, green loyalty points, or transit vouchers to the user's digital wallet.

Edge machine learning models trained on TensorFlow Lite and OpenCV are integrated using embedded camera modules to classify deformed containers, barcode brand IDs, and material transparency in real time with over 95% sorting accuracy, continuously improving through cloud model updates and automated retraining.