An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis.

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.

An Artificial Intelligence Approach for Expurgating Edible and Non-Edible Items.

In the pandemic of COVID-19, it is crucial to consider the hygiene of the edible and nonedible things as it could be dangerous for our health to consume infected things. Furthermore, everything cannot be boiled before eating as it can destroy fruits and essential minerals and proteins. So, there is a dire need for a smart device that could sanitize edible items. The Germicidal Ultraviolet C (UVC) has proved the capabilities of destroying viruses and pathogens found on the surface of any objects. Although, a few minutes exposure to the UVC can destroy or inactivate the viruses and the pathogens, few doses of UVC light may damage the proteins of edible items and can affect the fruits and vegetables. To this end, we have proposed a novel design of a device that is employed with Artificial Intelligence along with UVC to auto detect the edible items and act accordingly. This causes limited UVC doses to be applied on different items as detected by proposed model according to their permissible limit. Additionally, the device is employed with a smart architecture which leads to consistent distribution of UVC light on the complete surface of the edible items. This results in saving the health as well as nutrition of edible items.