Deep Spoil: A Deep Learning-Based model for Accurate Food Spoilage Detection

A systematic study demonstrated deep learning algorithm performance when detecting food spoilage during its implementation. Within the proposed methodology, deep learning algorithms, particularly convolutional neural networks (CNNs), perform automatic food image analysis to detect spoilage status. The research group operates deep learning models through testing them on extensive food image collection featuring various types of foods at different spoilage phases. The VGG CNN model operated as a transfer learning approach during food spoilage system construction. Scientists prepared the models through training them with spoiled food images to enable the identification of specific qualities in spoiled food. The research presented multiple variations of model parameters and architectural elements during its exploration of deep learning systems for food spoilage identification. The deep learning algorithms demonstrated precise food spoilage detection capabilities because they delivered dependable results per experimental findings.