The evolution of multipurpose sensors over the last decades has been investigated with the aim of developing innovative devices with applications in several fields of technology, including food industry. The project aims to develop a deep learning model that analyzes sensor data to evaluate food quality in real-time. Sensors like gas sensors (e.g., MQ series), DHT11 sensor, air quality sensors are integrated to capture the chemical and physical characteristics of perishable food items. The deep learning model classifies the food quality (e.g., fresh, stale, spoiled) and provides visual feedback. This can be highly beneficial in smart kitchens, food supply chains, and quality control labs. The project makes use of sensors like air quality, Methane gas sensor, DHT11 to detect the quality of food based on smell and gases which releases from spoiled items. The main controlling device of the project is Arduino UNO controller and Raspeberry Pi 3 B+ processor to achieve this task. This technology, when combined with IoT, is able to provide intimation to the user about quality of food. It’s just like a food inspection technology. More precisely, our system consists of MQ-3 Methane Gas sensor, MQ-135 air quality, DHT11, IR sensor which are interfaced to the Arduino provides the essential information needed for evaluating the quality of the food. In this IR sensor is used to detect the food presence in the chamber and based on the sensors data the Arduino analyzes the quality of food and this information is transmitted to Raspberry Pi3 B+ processor. The processor takes responsibility to display the monitored data received from Arduino UNO onto the LCD and uploads the food quality analysis into the thingspeak cloud. To perform this intelligent task, using Arduino is loaded with a program written in embedded ‘C’ language. To perform this task, Raspberry Pi processor is programmed using embedded ‘Linux’.      

The main objectives of the project are:

1. By using this project, we can identify the quality of food.
2. Sending intimation to the user using IOT.
3. Visible alerts using LCD display.

    The main building blocks of the project are:  

1. Power supply.
2. Raspberry Pi 3 B+ processor
3. ARDUINOP UNO.
4. MQ-3 Methane sensor.
5. MQ-135 Air quality sensor.
6. DHT11.
7. LCD display.
8. IR sensor.

      Software’s used:  

1. ARDUINO IDE compiler for Embedded C programming.
2. Express SCH for Circuit design.
3. Thingspeak technology.
4. Python Language.
5. Raspbian OS.

    Regulated Power Supply:

         

Block Diagram:  

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