A novel WiFi-based milk freshness detection method using image features and tensor construction

In the field of food safety, milk has become an indispensable beverage in people’s lives. Therefore, it is of great significance to detect milk freshness. Starting from the milk freshness detection, the perspective can be extended to liquid detection. Liquid detection has attracted much attention and has been applied in many fields. However, current liquid detection methods are either contact detection methods that can lead to sample damage, or require specialized instruments, expertise for operation or cumbersome hardware deployment. This paper introduces a non-contact milk freshness detection method based on image features and tensor construction. Unlike existing liquid detection methods, our method relies on ubiquitous WiFi signals to achieve non-contact and non-invasive milk freshness detection. The design intuition is that the WiFi signals will lead to different multipath propagation when passing through milk with different freshness, which can be used to detect milk freshness. We use existing commercial devices to collect WiFi signal data of milk with different freshness, denoise the collected data and transform the denoised data into multiple types of time-frequency images and spatial-temporal images, and input the images into the deep learning network to extract image features that contain richer and more comprehensive information, and then utilize the extracted image features to perform tensor construction to better retain the original time, frequency and spatial feature information, and apply 3D convolutional layer and fully connected layers for milk freshness detection. The experimental results show that our method achieves a high accuracy of 93.25% in detecting milk freshness.