An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

• Published in: Frontiers in public health Vol. 9; p. 816226
• Main Authors: Sonwani, Ekta, Bansal, Urvashi, Alroobaea, Roobaea, Baqasah, Abdullah M, Hedabou, Mustapha
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 12.01.2022
• Subjects: Artificial Intelligence; Food Microbiology; food spoilage detection; food spoilage prevention; IoMT; machine learning for health; Public Health; sensors; smart system; Temperature; smart system; food spoilage prevention; machine learning for health; sensors; food spoilage detection; IoMT
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2021.816226

Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.