An Efficient CNN-based Method for Classification of Red Meat Based on its Freshness

Red meat is one of the most popular varieties of meat in the eastern part of India. Consumption of prolonged accumulated or spoiled meat results in many fatal diseases. Traditional detection methods, such as sensory testing, physical and chemical testing, microbiological testing, and instrument analysis, are all complex, time-consuming, destructive, and uneconomical. In this study, we have designed an efficient, and non-destructive procedure for the classification of red meat based on its freshness using a novel convolutional neural network algorithm called “HarNet”. Our “HarNet” model gives better results and outperforms many pre-trained models like VGG16, VGG19, ResNet50, and InceptionV3. After testing and performing statistical analysis, our proposed model has achieved an accuracy of 80%. The F1-score value for the spoiled class is 0.89 and the recall value of 0.96 is the highest attained by the HarNet model, followed by the fresh class with 0.78 as the F1-score value and the recall value of 0.82 after testing. The results given by our proposed model are better than many of state of the art deep learning methods.