A Perishable Food Monitoring Model Based On Iot And Deep Learning To Improve Food Hygiene And Safety Management

Abstract

Food processing businesses play a crucial role in ensuring that people have access to nutritious food. These companies can either produce food directly for consumers or provide ingredients for other businesses to use in their products. Many food processing firms rely on interacting directly with customers to sell their products. However, this can be a challenge during the COVID-19 pandemic and other situations that limit face-to-face interactions. One potential solution to this issue is the use of computer vision to analyze food quality in an automated, non-invasive, and cost-effective way. This type of technology can help businesses ensure that they are providing high-quality products to their customers. A study has presented an OpenCV Python library-based system that uses the Convolution Neural Network (CNN) to identify and assess food quality. This system can perform tasks such as detecting the type of fruit and rating its quality based on accurate and trustworthy data. To sort the fruits, the suggested system includes a microcontroller, instruments, a camera, and a transporter belt setup. This helps to ensure that the fruits are separated accurately and efficiently. In addition, the system uses sensors to monitor the storage environment and transmits this data over the cloud using Internet of Things technology. This helps to maintain the quality of the fruit for longer periods of time, which is important for businesses that want to provide fresh and tasty products to their customers. Overall, the use of computer vision and other advanced technologies can help food processing businesses provide high-quality products to their customers in a cost-effective and efficient manner.