Development of CNN Model to Avoid the Food Spoiling Level

Food grains are sensitive to deterioration owing to precipitation temperature, humidity, and several other variables; thus, researchers are working toward novel approaches to preserve the dietary value of food to improve the shelf lifespan of food. Therefore, reliable methods of monitoring food deterioration are necessary to maintain current food safety standards. A prototype has been developed to monitor food quality and manage storage systems within domestic settings. The CNN (Convolutional Neural Network) model was initially utilized for fruit and vegetable classification. The proposed system utilizes actuators and sensors to monitor the emission of gases, humidity, and the temperature of vegetables and fruits to assess the degree of food spoilage. Furthermore, this measure effectively regulates the surrounding conditions and minimizes the occurrence of food deterioration whenever feasible. Customers are notified through text message when the food they ordered is about to deteriorate based on the product’s freshness and condition. The used model has a confirmed accuracy of 96%. Some types of food could extend their storage life by two days, meaning the experiment succeeded.