## 4 Discussion  
In this section, we analyze and discuss the findings of our study, in which we focused on improving automated tomato harvesting through a novel hybrid model integrating CNNs and PAFs.  
1. The core achievement of our research is the development of a hybrid model that synergizes CNNs with PAFs, which resulted in a remarkable recognition accuracy on a diverse dataset. This high accuracy, which was consistent under various environmental conditions and tomato varieties, is evidence of the model's robustness and adaptability. The integration of DL with state-of-the-art image-processing techniques made our algorithm significantly outperform existing algorithms, which marks a substantial advancement in the field of agricultural automation.
2. Despite the overall success of our model, it encountered challenges in key point detection, particularly for images that contained small or clustered fruits. These errors, detailed in Table 1, provide crucial insights into the limitations of the current model. The higher error rates observed for the branching points caused by their frequent merging with the stem parts highlighted areas for future refinement. By contrast,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Axis**} & **Center of Gravity** & \multirow{2}{*}{**Calyx**} & **Abscission Zone** & **Branch Point** & \multirow{2}{*}{**Total**} \\ \hline \(X\) (pixel) & 5.3 & 2.6 & 4.1 & 10.6 & 5.65 \\ \(Y\) (pixel) & 6.2 & 3.0 & 4.7 & 13.1 & 6.75 \\ Overall average score (\%) & 97.2 & 96.7 & 95.5 & 96.2 & 96.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Position errors for the detected key points.  
the low number of pixel errors for the calyx and the tomato's center-of-gravity point indicate their geometric importance in robotic systems in terms of achieving accurate harvest point identification.
3. The findings have significant implications for the field of robotic harvesting. The model's ability to accurately identify and position tomatoes paves the way for more efficient, precise, and scalable robotic harvesting solutions. This advancement could lead to a reduction in manual labor and an increase in harvesting efficiency, which are crucial for meeting the growing demands of the agricultural industry.
4. Another critical aspect of our study is the model's performance under various real-world agricultural conditions. The integration of multispectral analysis and data-enhancement techniques enabled the model to effectively adapt to various environmental conditions and characteristics of tomato varieties. This adaptability is essential for the practical application of the technology in diverse agricultural settings.
5. Our study marks a significant step forward in agricultural automation and also provides opportunities for further research. Areas for future improvement include enhancing the model's ability to process images that contain small or clustered fruits and refining the detection of more challenging key points, such as branching points. Additionally, extending the model's application to other crops and agricultural practices could broaden its impact on the industry.  
To summarize, our study demonstrates a significant breakthrough in the automation of agricultural processes, particularly in the context of tomato harvesting. The developed model not only offers high accuracy and adaptability but also provides a foundation for further advancements in the field. This model has the potential to transform agricultural practices by contributing to the sustainability and efficiency of food production globally.