## 4 Discussion  
Our study demonstrates the crucial role of HPC in image processing specifically using distributed DL when classifying different types of weeds. By using HPC, we significantly reduced the processing times and the effort required to train models or implement computationally demanding algorithms. Compared with the use of a single computer, our proposal  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN Model** & \begin{tabular}{c} **Numbers of** \\ **Processes** \\ \end{tabular} & \begin{tabular}{c} **Processing** \\ **Time (HPCC)** \\ \end{tabular} & \begin{tabular}{c} **Processing** \\ **Time (WS)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(HPCC)** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy (WS)** \\ \end{tabular} &
\begin{tabular}{c} **Epochs** \\ \end{tabular} \\ \hline VGG16 & 4 & 147 min 8.656 s & 231 min 19.132 s & 0.77 & 0.78 & 25 \\ InceptionV3 & 4 & 54 min 30.690 s & 52 min 26.876 s & 0.62 & 0.61 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results obtained during training with four cores on the workstation and HPCC.  
Figure 8: Comparison of the accuracy values obtained by applying different process values in the HPCC.  
Figure 7: Graph of the times obtained for each of the training sessions of the InceptionV3 and VGG16 models.  
offers a considerable benefit in terms of efficiency and profitability, making it a valuable alternative for the university and scientific community that may not have access to an HPC infrastructure listed within the TOP500 or that can access a service through the cloud. Our research focused on PDI and AI applications, where we extracted important features such as characteristic plant greens to improve the weed-type classification. We used the color-based segmentation method to preprocess the images, which provided good results.  
When implementing the HPCC, we ensured the control and correct installation of the application versions to ensure compatibility with the hardware installed in the computing nodes. The application versions that worked successfully for this project were CUDA 11.7, NCCL 2.1, CuDNN 7.5, and OpenMPI 4.0. We also ensured that the versions of the Tensorflow, Keras, and Horovod libraries were compatible with these applications. It is important to highlight the fact that the implementation process was very complex and took a long time since when the libraries did not match or were not compatible, the HPCC did not work correctly.  
Our research results showed that in using the HPCC, we achieved a maximum accuracy of 82% with a training time of over 4 h, whereas the work of [PERSON] et al. [22] reported an average accuracy between 95.1% and 95.7% with a training time of 13 h using a single graphics card.  
One of the critical metrics obtained in our research was accuracy, where we achieved the highest value of 0.82 using a single process in the proposed HPCC. However, the longer training times may have been due to the need for more epochs to achieve better loss values in the CNN VGG16 model. The number of epochs depended on the number of processes introduced to the HPCC, which explained why the metric values varied, as shown in Tables 4-6.  
An interesting finding was that applying six processes in the HPCC resulted in the best time for both CNN models. Loading and distributing two processes as a parameter in each node, we were able to utilize 50% of the processor capacity in each computing node. This trend suggests that utilizing eight processes in an HPCC with additional computing nodes could yield even better processing times.  
Finally, our research indicates that increasing the number of computing nodes could improve the model's performance and execution times. Despite the limitations, our proposal offers a low-cost alternative that can be applied to different research areas that require HPC, such as PDI and AI.