## 5 Experimental Results and Discussion
### Results of Simplified AI Models with Post-Training Weight Quantization  
Table 10 presents the results for the number of parameters, memory size, and test accuracy of these simplified state-of-the-art AI models following the application of the 8-bit weight quantization technique. When comparing the results in Table 9 with those in Table 10, it is evident that the memory size in Table 10 was significantly reduced to just a few megabytes (MB). As an illustration, the quantized simplified model of the L-w Xception 1 model with a growth rate of 0.25 achieved a remarkable test accuracy of 84.73% while utilizing only a 0.69 MB of memory. Therefore, this lightweight model shows great promise for deployment in micro-controllers with a memory budget of 1 MB. In comparison to the most accurate state-of-the-art model in the literature (Xception model in Table 8), this proposed model (the quantized simplified L-w Xception 1 model with a growth rate of 0.25 in Table 10) achieved a remarkable memory size reduction of 350 times, while experiencing only a minor decrease in classification accuracy, i.e., approximately 4.54%.  
Figure 8: DenseNet121 with a growth rate of 10.  
Figure 7: Performance comparison of test classification accuracy vs. memory footprint of proposed simplified AI models and state-of-the-art AI models in the literature.  
In the study of object classification, a confusion matrix is a tabular representation frequently utilized to assess the performance of a classification model. I present a comprehensive summary of the model's prediction and their agreement with the ground-truth labels for each category. This overview is conducted for the original Xception model (Figure 10) and the newly proposed L-w Xception model with a width factor of 0.25 (Figure 11). As shown in Figure 10, even though this Xception model requires a large memory space, it predicted the categories of rice, mung bean, and millet very well. The classification accuracy for the category of cat/dog food was 79%. In contrast, Figure 11 demonstrates excellent predictions for rice, sunflower seed shell, and mung bean. I think this is probably due to their distinctive features. These object categories possess unique and distinguishable visual features that are easily recognized by this L-w Xception 1 model with a growth rate of 0.25. These distinct features enable accurate predictions, as the model can confidently identify patterns specific to these categories. Regarding the prediction of cat/dog food, Figure 11 shows an accuracy of 83%. This improvement implies that the simplified Xception 1 model has an enhanced capability in identifying pet food with a much smaller memory size. Both models exhibit a weakness in accurately predicting soybeans, possibly attributed to their yellow color, which can closely resemble the color of the floor or the surrounding  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantized Simplified** & **The Number of Parameters** & **Memory Size** & **Test Accuracy** \\ \hline L-w Xception 1 (alpha = 0.5) & 2,421,120 & 2.51 MB & 87.80\% \\ \hline L-w Xception 1 (alpha = 0.25) & 622,160 & 0.69 MB & 84.73\% \\ \hline L-w Xception 1 (width = 0.17) & 355,794 & 0.42 MB & 76.76\% \\ \hline DenseNet121 (growthrate = 4) & 169,086 & 0.4 MB & 78.30\% \\ \hline DenseNet121 (growthrate = 8) & 527,976 & 0.7 MB & 81.20\% \\ \hline DenseNet121 (growthrate = 10) & 784,833 & 1.0 MB & 83.36\% \\ \hline MobileNet (alpha = 0.5) & 833,640 & 0.9 MB & 76.61\% \\ \hline MobileNet (alpha = 0.53) & 930,435 & 1.0 MB & 78.75\% \\ \hline MobileNet (alpha = 0.75) & 1,839,128 & 1.9 MB & 80.52\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test accuracy and memory usage for simplified state-of-the-art AI models with 8-bit weight quantization.  
Figure 9: L-w Xception 1 with a width factor of 0.25.  
environment. This similarity in color makes it challenging for the model to distinguish soybean from the background, especially if the background floor color is light or white.