## 4 Results and Discussions
### Meat Quality Databases  
This work is experimented on two publicly available databases for assessing meat quality. The first database has two classes. The images were obtained from a supermarket in Izmir, Turkey. The goal of this database is to facilitate a collaboration between Izmir University of Economics and industry [25]. This dataset was compiled in order to create a deep-learning-based meat quality assessment system. Images were captured using an IP camera and had a resolution of \(1280\times 720\). A total of 1896 images with equal instances of fresh and spoiled red meat samples are included in this dataset. Figure 3 depicts the sample images from the database. Spoiled meat images have lower pixel intensities when compared to fresh red meat images. This color characteristics can be effectively detected by deep CNN models.  
The second database employed in this work is a multi-class meat freshness database [27]. The data include 1815 training images and 451 validation images. All images are of red means, with a dimension of \(416\times 416\). The images are categorized into fresh, half-fresh, and spoiled classes. The sample images are depicted in Figure 4. The distribution is largely concentrated on the brighter pixels (255) for fresh meats, while the distribution is concentrated on the dark pixels (0) for the spoiled meats. Half-fresh meat also has a more concentrated distribution on the lighter pixels than spoiled meats. These differences in pixel intensity distribution can be effectively captured by the suggested ConvNet-24 model. For convenience, the terminology MEAT2C and MEAT3C are utilized in this paper to represent the two-class and multi-class meat quality datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Layer** & **Feature Maps** & **Stride** & **Activation** & **Trainable** \\
**Input** & 1 & - & \(256\times 256\times 3\) & 0 \\ CL1 & 16 & 1 & \(254\times 254\times 16\) & 160 \\ BNL1 & 16 & - & \(254\times 254\times 16\) & 32 \\ RL1 & 16 & - & \(254\times 254\times 16\) & - \\ PL1 & 16 & 2 & \(127\times 127\times 16\) & - \\ CL2 & 32 & 1 & \(125\times 125\times 32\) & 4640 \\ BNL2 & 32 & - & \(125\times 125\times 32\) & 64 \\ RL2 & 32 & - & \(125\times 125\times 32\) & - \\ PL2 & 32 & 2 & \(62\times 62\times 32\) & - \\ CL3 & 64 & 1 & \(60\times 60\times 64\) & 18,496 \\ BNL3 & 64 & - & \(60\times 60\times 64\) & 128 \\ CL4 & 128 & 1 & \(58\times 58\times 128\) & 73,856 \\ BNL4 & 128 & - & \(58\times 58\times 128\) & 256 \\ RL4 & 128 & - & \(58\times 58\times 128\) & - \\ PL4 & 128 & 2 & \(29\times 29\times 128\) & - \\ CL5 & 128 & 1 & \(25\times 25\times 128\) & 409,728 \\ BNL5 & 128 & - & \(25\times 25\times 128\) & 256 \\ CL6 & 128 & 1 & \(21\times 21\times 128\) & 409,728 \\ BNL6 & 128 & - & \(21\times 21\times 128\) & 256 \\ RL6 & 128 & - & \(21\times 21\times 128\) & - \\ PL6 & 128 & 2 & \(10\times 10\times 128\) & - \\ FCL & - & - & \(1\times 1\times 2\) & 76,806 \\ Softmax & - & - & \(1\times 1\times 2\) & - \\ Classification & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 2: Layer information of the ConvNet-24.  
Figure 4: Sample meat images from MEAT3C database: (**a–c**) sample fresh meat images, (**d–f**) sample half-fresh images, and (**g–i**) sample spoiled meat images.  
Figure 3: Sample meat images from MEAT2C database: (**a–c**) indicate sample fresh red meat images, whereas (**d–f**) depict sample spoiled red meat images.