This project presents an intelligent refrigerator monitoring system for real-time detection of spoilage in fruits, specifically apples and bananas, using a combination of AI/ML techniques and sensor-based analysis. The system integrates multiple sensors—including gas (MQ-135 for ethylene/methane detection), temperature and humidity (DHT11), IR, and NIR sensors—along with a camera module to continuously monitor the condition of stored produce. An Arduino Nano collects sensor data and transmits it to a Raspberry Pi 4, which serves as the central processing unit. The Raspberry Pi performs image processing and machine learning–based classification to determine the freshness status of fruits (good or spoiled). The camera enables live video streaming, allowing users to visually inspect the fruits remotely. All collected data, including environmental parameters, gas concentration, and fruit condition, are displayed on a web-based dashboard in real time. The system also includes an LCD for local display and a buzzer alert to notify users when spoilage is detected. By combining sensor fusion and AI-based image analysis, the proposed system enhances food safety, reduces waste, and enables smart refrigerator management through remote monitoring and decision-making.        

The main objectives of this project are:

• To design and develop an intelligent refrigerator monitoring system for detecting spoilage in fruits like apples and bananas.
• To integrate multiple sensors (gas, temperature, humidity, IR, and NIR) for real-time environmental and freshness monitoring.
• To implement AI/ML-based image processing techniques for classifying fruits as fresh (good) or spoiled (bad).
• To enable live video streaming of fruits using a camera for remote visual inspection.
• To develop a web-based dashboard for displaying sensor data, fruit selection, and spoilage status in real time.
• To provide early warning alerts (buzzer/notification) when spoilage or abnormal conditions are detected.
• To ensure seamless communication between Arduino Nano and Raspberry Pi for efficient data processing.
• To reduce food wastage by enabling timely detection and monitoring of fruit quality.
• To create a low-cost, user-friendly smart refrigeration system for domestic and commercial use.

        The major building blocks of this project are:  

1. Regulated power supply.
2. AC – DC converter.
3. Raspberry pi4.
4. SD card.
5. Pi camera.
6. Arduino Nano.
7. MQ-135 sensor.
8. Methane sensor.
9. DHT11 sensor.
10. IR sensor.
11. NIR sensor.
12. Arduino Nano.
13. LCD display.
14. Buzzer.

      Software’s used:  

• Raspbian OS.
• Machine learning & Artificial Intelligence (AI) algorithm.
• Express SCH for Circuit design.
• WEB application

          Block Diagram:        

video: