Monitoring Fresh Fruit and Food Using Iot and Machine Learning to Improve Food Safety and Quality

Abstract

Consumer demands and expectations have an effect on the general quality of a good or service. Another way to describe "quality" is as the whole of all the characteristics that go into creating products and services that satisfy the consumer. Thanks to the efforts of importing nations, the quality of several imported goods has recently improved. By ensuring that imported food is secure for human consumption before it is released, it also protects food from other countries. This article discusses a method for keeping track of perishable commodities based on machine learning and Internet of Things.  The suggested system involves taking pictures using high-resolution cameras and uploading them to a cloud server utilisingIoT devices. Before being uploaded to a cloud server, these photos are segmented using the K-means clustering algorithm. Following the extraction of attributes from the pictures using the principal component analysis approach, trained machine learning models are used to classify the images. This proposed approach makes use of Internet of Things, image processing, and machine learning to monitor perishable food.