Development of CNN Model to Avoid Food Spoiling Level

Abstract

Food spoilage is a pervasive issue that contributes to food waste and poses significant economic and environmental challenges worldwide. To combat this problem, we propose the development of a Convolutional Neural Network (CNN) model capable of predicting and preventing food spoilage. This paper outlines the methodology, data collection, model architecture, and evaluation of our CNN-based solution, which aims to assist consumers, retailers, and food producers in minimizing food waste. Researchers are working on innovative techniques to preserve the quality of food in an effort to extend its shelf life since grains are prone to spoiling as a result of precipitation, humidity, temperature, and a number of other factors. In order to maintain current standards of food quality, effective surveillance systems for food deterioration are needed. To monitor food quality and control home storage systems, we have created a prototype. To start, we used a Convolutional Neural Network (CNN) model to identify the different types of fruits and vegetables. The suggested system then uses sensors and actuators to check the amount of food spoiling by monitoring the gas emission level, humidity level, and temperature of fruits and vegetables. Additionally, this would regulate the environment and, to the greatest extent feasible, prevent food spoiling. Additionally, based on the freshness and condition of the food, a message alerting the client to the food decomposition level is delivered to their registered cell numbers. The model used turned out to have a 96.3% accuracy rate.