Measuring water holding capacity in pork meat images using deep learning

Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this work, we used the Deep Learning (DL) architecture named U-Net to estimate water holding capacity (WHC) from filter paper images of pork samples obtained using the press method. We evaluated the ability of the U-Net to segment the different regions of the WHC images and, since the images are much larger than the traditional input size of the U-Net, we also evaluated its performance when we change the input size. Results show that U-Net can be used to segment the external and internal areas of the WHC images with great precision, even though the difference in the appearance of these areas is subtle.

Automated summary

In this study, the U-Net deep learning architecture was used to estimate the water holding capacity (WHC) of pork samples by analyzing filter paper images. The results showed that U-Net can accurately segment the external and internal areas of the filter paper images, even with subtle differences in appearance.