IoT Based Food Freshness Detection by Using Python

Abstract

In the contemporary landscape of interconnected devices and the Internet of Things (IoT), the pursuit of sustainable practices has led to innovative solutions in diverse domains. This project introduces an IoT-based system for food freshness detection, leveraging the capabilities of the Python programming language. The primary objective is to design a versatile and accessible platform that monitors the freshness of perishable goods in real-time, contributing to reduced food wastage and enhanced consumer awareness. The project centers around the integration of sensors, particularly gas sensors, with a Raspberry Pi microcontroller. These sensors capture volatile compounds emitted during the degradation of food items, providing crucial data for freshness assessment. The Python programming language is employed to orchestrate sensor data processing, implement intelligent freshness determination logic, and facilitate communication with IoT platforms. Key components of the project include the establishment of MQTT communication for real-time data transfer, integration with cloud platforms (such as AWS IoT or ThingSpeak) for data storage and visualization, and the optional development of a user interface for remote monitoring. Through rigorous testing and calibration, the system aims to achieve a reliable and accurate assessment of food freshness across diverse environmental conditions. The anticipated outcomes include a functional and scalable IoT-based food freshness detection system. By providing users with real-time insights into the condition of perishable goods, the project strives to empower consumers to make informed decisions, reduce food wastage, and contribute to sustainable food management practices.

Introduction

I. INTRODUCTION

In a world where food safety and sustainability are paramount concerns, the need for innovative solutions to monitor and ensure the freshness of perishable goods has become increasingly significant. The advent of Internet of Things (IoT) technologies, coupled with the versatility of the Python programming language, offers a promising avenue for the development of intelligent devices dedicated to this purpose. This project focuses on the creation of a sophisticated yet accessible device designed to detect and evaluate the freshness of food items in real-time. By employing Python as the primary programming language and integrating various sensors with a Raspberry Pi microcontroller, this solution aims to provide a cost-effective and scalable approach to food freshness monitoring. This project focuses on the creation of a sophisticated yet accessible device designed to detect and evaluate the freshness of food items in real-time. By employing Python as the primary programming language and integrating various sensors with a Raspberry Pi microcontroller, this solution aims to provide a cost-effective and scalable approach to food freshness monitoring. This project focuses on the creation of a sophisticated yet accessible device designed to detect and evaluate the freshness of food items in real-time. By employing Python as the primary programming language and integrating various sensors with a Raspberry Pi microcontroller, this solution aims to provide a cost-effective and scalable approach to food freshness monitoring.

II. LITERATURE REVIEW

 The advent of Internet of Things (IoT) technologies and the versatile nature of the Python programming language have spurred innovative solutions in various domains, including food freshness detection. The pressing global issues of food safety, waste reduction, and sustainable resource management have prompted researchers and engineers to explore intelligent systems capable of real-time monitoring and assessment of perishable goods.

To guarantee consistent and comparable results across various freshness detection techniques, standardization and calibration protocols must be created. It is necessary to build real-time monitoring technologies to enable continuous evaluation of food freshness across the supply chain.

Processes for gathering data, analyzing it, and making decisions can be improved with integration with Internet of Things (IoT) technology. To encourage the use of freshness detection systems in many areas of the food business, cost-effectiveness and scalability are crucial factors.

To address issues with the gathering and processing of sensitive data, it is also necessary to put in place privacy protection and data security safeguards.

In conclusion, the study of food freshness detection methods offers a solution to raise food quality, lessen waste, protect consumers, and encourage sustainability in the food business. Stakeholders can use these strategies and revolutionize how food freshness is monitored, controlled, and optimized by resolving the difficulties and concentrating on next developments. A more effective, open, and customer-focused food system will be possible with the integration of cutting-edge technologies and fresh ideas. The presented strategies have a lot of potential, but there are obstacles that must be overcome before they can be widely used.

1. Sensor Technologies for Food Quality: Various studies have underscored the significance of sensor technologies in monitoring food quality. Gas sensors, in particular, have been extensively researched for their ability to detect volatile compounds emitted during the spoilage of perishable goods. These compounds, often indicative of freshness degradation, serve as essential data points for real-time monitoring systems.
2. Integration Python in IoT Application: Python's popularity in IoT applications has been well-documented. Its ease of use, versatility, and extensive libraries make it an ideal choice for developing applications that involve sensor integration and data processing. Researchers have utilized Python to seamlessly connect sensors, particularly gas sensors, with IoT platforms, allowing for efficient data analysis and interpretation.
3. Raspberry Pi as a Microcontroller: The Raspberry Pi has emerged as a prominent microcontroller for IoT-based projects. Its GPIO pins enable straightforward connections with various sensors, making it a preferred choice for developers. Studies have demonstrated the successful integration of Raspberry Pi with gas sensors and Python scripts for real-time freshness monitoring.
4. Gas Sensors for Freshness Detection: Gas sensors have been a focal point in research due to their ability to detect specific gases released during food spoilage. Ethylene, a common gas emitted by ripening fruits, serves as a key indicator. Researchers have explored the integration of gas sensors with IoT platforms to develop systems capable of not only monitoring freshness but also predicting shelf life based on gas emissions.
5. Communication Protocols in IoT Based System: Communication protocols play a crucial role in facilitating seamless data transfer between devices in an IoT ecosystem. MQTT, in particular, has been widely employed for its lightweight nature and efficiency. Studies have showcased the use of MQTT in Python scripts to establish communication between the Raspberry Pi and cloud-based IoT platforms, ensuring real-time data transmission.
6. Cloud integration for Data Storage and Analysis: Cloud platforms, such as AWS IoT and ThingSpeak, have been integral in storing and analyzing data from IoT devices. Researchers have integrated Python scripts with these platforms, allowing for centralized data storage, visualization, and analysis. Cloud integration enhances the scalability and accessibility of IoT-based food freshness detection systems.
7. Challenges and Future Direction: While advancements have been made, challenges remain. Calibration of sensors for different food types, adapting algorithms to diverse environmental conditions, and ensuring the security of IoT communication are areas that require further exploration. Future research directions may involve the application of machine learning techniques for more advanced freshness prediction.

III. METHODOLOGY

 The development of an IoT-based food freshness detection system using Python involves a systematic approach that encompasses hardware setup, software implementation, cloud integration, and testing. The following detailed methodology outlines the step-by-step process for creating a robust and scalable solution.

A. Hardware Setup

1. Utilize a Raspberry Pi as the central microcontroller for its flexibility and GPIO capabilities.
2. Integrate gas sensors to detect specific gases emitted by food during degradation.
3. Connect temperature and humidity sensors to capture additional environmental data.
4. Power the system with a suitable power source and ensure proper wiring on a breadboard.

V. Software Implementation

1. Develop a Python script to read data from the gas, temperature, and humidity sensors.
2. Implement freshness determination logic based on sensor readings.
3. Use the ‘paho-mqtt’ library to establish MQTT communication for real-time data transfer.

C. Cloud Integration

1. Create an account on an IoT cloud platform (e.g., AWS IoT, ThingSpeak).
2. Configure the Raspberry Pi to publish freshness data to the cloud using MQTT.
3. Set up data visualization tools on the cloud platform for monitoring.

D. User Interface (Optional)

1. Develop a user interface, either web-based or mobile, for users to monitor freshness remotely.
2. Implement features such as real-time alerts and historical data tracking.

E. Testing and Calibration

1. Conduct thorough testing to ensure accurate sensor readings and freshness determination.
2. Calibrate sensors as needed to account for variations and environmental factors.

IV. PROPOSED SYSTEM

1. PH Sensor: A pH sensor helps to measure the acidity or alkalinity of the water with a value between 0-14. When the pH value dips below seven, the water starts to become more acidic. Any number above seven equates to more alkaline. Each type of pH sensor works differently to measure the quality of the water.
2. MQ135 gas Sensor: A device that is used to detect or measure or monitor the gases like ammonia, benzene, sulfur, carbon dioxide, smoke, and other harmful gases are called as an air quality gas sensor. The MQ135 air quality sensor, which belongs to the series of MQ gas sensors, is widely used to detect harmful gases, and smoke in the fresh air. This article gives a brief description of how to measure and detect gases by using an MQ135 air quality sensor.
3. Humidity Sensor: The humidity sensor is a device that senses, measures, and reports the relative humidity (RH) of air or determines the amount of water vapor present in gas mixture (air) or pure gas. Humidity sensing is related to a water adsorption and desorption process.
4. Temperature Sensor: A temperature sensor is a device that detects and measures hotness and coolness and converts it into an electrical signal.
5. ESP8266 wifi: The ESP8266 WiFi Module is a self contained SOC with integrated TCP/IP protocol stack that can give any microcontroller access to your WiFi network. The ESP8266 is capable of either hosting an application or offloading all Wi-Fi networking functions from another application processor.

V. FUTURE WORK

Our future implementation is to add more food items like vegetables and meat varieties. This system can be employed in the food packaging industries to check the quality of food items with high speed and accuracy. This method of detection can also be useful in foodbased product-type Industries to check the manufacturing defects by changing the trained model. Based on the future requirement it can be easily updated and changes are made as per business requirement.

Conclusion

In conclusion, methods for determining food freshness are essential for providing high-quality food items, cutting waste, improving consumer safety, and advancing sustainable food business practices. The numerous methods used for objective freshness evaluation, such as spectroscopy, gas sensors, and computer vision, have all been thoroughly reviewed in this study paper The uses for food freshness detecting methods are numerous and varied. They can be used in supply chain management, assurance of consumer safety, shelf-life forecasting, and food waste reduction operations. The ability to sort and separate food items allows for improved inventory management and waste reduction. This is made possible by the implementation of objective freshness assessment methods. The effectiveness of the supply chain can be increased by quick decision-making regarding storage, transit, and distribution with the use of real-time freshness monitoring. Furthermore, freshness detection methods reduce the risk of foodborne infections by preventing the intake of damaged or possibly dangerous food. The presented strategies have a lot of potential, but there are obstacles that must be overcome before they can be widely used. To guarantee consistent and comparable results across various freshness detection techniques, standardization and calibration protocols must be created. It is necessary to build real-time monitoring technologies to enable continuous evaluation of food freshness across the supply chain. Processes for gathering data, analyzing it, and making decisions can be improved with integration with Internet of Things (IoT) technology. To encourage the use of freshness detection systems in many areas of the food business, cost-effectiveness and scalability are crucial factors. To address issues with the gathering and processing of sensitive data, it is also necessary to put in place privacy protection and data security safeguards.

References

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Copyright

Copyright © 2023 Dr. Manisha Pise, Gaurav B. Nagarkar, Utkarsha Punekar, Ruchika Katore, Zainab Sheikh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.