Automated Food Spoilage Detection Using Deep Learning: A CNN-Based Approach

Food spoilage detection is critical in ensuring food safety and reducing waste. In this work, we offer a new neural network model, rotOrNot, intended for image analysis-based rotten food detection. Our method focuses on accurately identifying spoiled food, precisely spotting rot. The proposed method meets a vital demand for reducing food waste and guaranteeing food safety. Through several performance criteria, the proposed model was thoroughly tested and showed better performance than current approaches. rotOrNot specifically obtained training accuracy of 99.4%, validation accuracy of 96.95%, an outstanding ROC-AUC score of 99.97296%, and a test accuracy of 97.52%. Moreover, rotOrNot shows the fastest training time and exceeds the previous models in accuracy, so it is a quite effective tool for practical usage. This work offers a useful instrument for the automatic food rotting detection, therefore improving food safety procedures and maybe reducing food waste.