An IoT and Machine Learning-Based Model to Monitor Perishable Food towards Improving Food Safety and Quality

Abstract

Increased quantities of the same sort of item are not nearly as critical to client happiness as a high-quality product. The requirements and expectations of the consumer have an impact on the overall quality of a product or service. The term “quality” may also be defined as the sum total of all the features that contribute to the production of goods and services that are satisfactory to the consumer. Certain imported commodities have lately seen an improvement in quality thanks to efforts by importing nations. Additionally, it safeguards food imported from other nations by confirming that it is safe for human consumption before it is released. This article describes a technique for monitoring perishable goods that is based on the Internet of Things and machine learning. Pictures are recorded using high-resolution cameras in this suggested architecture, and then these images are sent to a cloud server using Internet of Things devices. When uploaded to a cloud server, these photos are segmented using the K-means clustering method. Then, using the principal component analysis technique, features are extracted from the photos, and the images are categorized using machine learning models that have been trained. This proposed model makes use of the Internet of Things, image processing, and machine learning to monitor perishable food.