## 4 Results and Discussion
### ViT and Global Fine-Grained Features Achieved Explainable Features Representation
To verify the performance of ViT in image encoding, both HE-CNN [9] and CNN [8] from related works replaced ViT as image encoders for global fine-grained orchid features, and the results are shown in Table 3. It can be observed that the performance of HE-CNN and CNN is inferior to ViT for both the validation and test sets. CNN gradually extracts local features through convolutional layers. Although multiple layers can be stacked to capture a larger receptive field, the inherent local convolution operations may limit its ability to effectively capture global dependencies. HE-CNN combines a global prediction network (GPN) for global feature extraction and a local prediction network (LPN) for local feature extraction, aiming to capture both global and local information. However, as it is still based on the CNN architecture, it remains constrained by the locality of convolution operations. In contrast, ViT can directly capture complex global information of the entire image by self-attention without relying on local convolutions. Therefore, ViT is more suitable for extracting images such as _Rclb. f._ that rely on global fine-grained features. Combined with text features, the model can better understand the relationship between _Rclb. f._ features and value, thereby accurately establishing a digital indicator system for _Rclb. f._ quality evaluation. The performance of Transformer Encoder was further verified against existing commonly used feature encoders, detailed in Table 4. Initially, the Transformer Encoder employed for processing the combined features of orchids was replaced with the VQ-VAE Encoder. For the #1 training, the performance indicated minor decline from Transformer-based model. However, the #2 and #3 training showed a noticeable performance decline compared to the Transformer Encoder. VQ-VAE Encoder created a static, discrete codebook representation of the input, which limited its flexibility in capturing the nuanced variations \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Encoder Input**} & \multicolumn{3}{c}{**Accuracy of Validation Set**} & \multicolumn{3}{c}{**Accuracy of Test Set**} \\ \cline{2-7}  & **HE-CNN** & **CNN** & **ViT** & **HE-CNN** & **CNN** & **ViT** \\ \hline \hline \multicolumn{7}{c}{Numerical features \(\otimes\) RGB \(\oplus\) global feature from HE-CNN, CNN or ViT} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy’s comparison of development set and test set with HE-CNN, CNN and ViT. in orchid features [33]. The VQ-VAE Encoder is used to generate compact, discrete representations, only for datasets with well-defined and consistent patterns [34; 35]. However, the _Rchb. f._ dataset exhibited significant feature sparsity and inconsistency caused by missing values, and is not an ideal input for the VQ-VAE Encoder. The Transformer Encoder was then replaced with C-VAE Encoder. Three identical trainings were performed, resulting in a further performance decrease compared to both Transformer Encoder and VQ-VAE Encoder. The conditional generation of C-VAE is designed to generate outputs based on fixed conditions, which cannot capture the full range of variability and nuances of orchids [36; 37]. Additionally, the variational method introduced variability while processing missing data based on the probability framework [38], leading to poor performance in the classification task. Conversely, the Transformer Encoder can dynamically focus on important features through self-attention spontaneously [39], rather than static codebook and fixed conditions generation, providing adaptability and good performance that is not achieved by rigid structures of VQ-VAE Encoder and C-VAE Encoder, and becoming an excellent choice for orchid quality grading model. In summary, through the integration of Transformer Encoder's self-attention and the combination of global fine-grained features, RGB and text features, this model achieved accurate grading of _Rchb.