## 4 Results and Discussion
### ViT and Global Fine-Grained Features Achieved Explainable Features Representation  
Petal color and textual information alone are not enough to distinguish the value of different qualities of orchids. The incorporation of ViT was crucial for extracting global fine-grained features from orchid petals, such as shape and texture of petals. The feature vector was concatenation with the output from the previous dot product operation of the embedded feature indices and corresponding values, as shown in Exp 3 in (Figure 6a). The accuracy of validation set soared to 93.39% after 39 epochs, while the test set accuracy reached 91.86% (Figure 6b). The improvement underscores the efficacy of ViT in discerning subtle patterns in the petal images that were not captured by color information alone [27]. By focusing on all locations and taking their weighted average to compute the response at a given location in image sequence, self-attention within ViT allowed the model to selectively focus on different parts of the petal images and analyzed the spatial hierarchical interrelations among petal features [28]. SHAP analysis post-ViT integration revealed that 'Seeding number' had the least impact on the model's predictions, indicating a shift in feature importance hierarchy (Exp 3 in Figure 6d). This shift emphasized the model's capacity to prioritize the most informative visual and textual cues for grading, highlighting ViT's nuanced understanding of _Rchb. f._ quality. Figure 5: (**a**) Illustration of model architecture with UNet and numerical features extracted from textual data. (**b**) The value curves of Cross Entropy Cost and accuracy of the model. (**c**) Radar chart of feature importance based on SHAP values. Figure 6: (**a**) Illustration of model architecture with UNet, ViT, and numerical features extracted from textual data (Exp 3) and concatenation operation for connecting ViT’s output to Transformer Encoder (Exp 4). The value curves of Cross Entropy Cost and accuracy of (**b**) Exp 3 and (**c**) Exp 4. (**d**) Radar chart of feature importance based on SHAP values. Concatenation Replaced Dot Production Reflecting the Relationship Between Key Features and Value Adequately  
The dot product in feature integration reduces the dimensionality of the output of ViT. Since the dot product is highly sensitive to the scale of the feature values, it can also merge multiple vectors into a scalar or a shorter vector, and the values with opposite signs will be canceled out, resulting in information loss [29, 30]. The transformation of the integration strategy of feature vectors refined the model architecture. The dot product operation between ViT's output and previous dot product operation result was deprecated, and optimized to concatenation operation, as shown in Exp 4 in (Figure 6a). The accuracy of validation set and test set has been further improved to 95.19% and 93.13%, respectively (Figure 6c). The concatenation operation preserved original information contained in the global fine-grained petal features of ViT, compensating for the loss caused by the dot product operation [31]. The output of the concatenation operation provided an optimal combined input to Transformer Encoder adaptively, thus addressing the shortcomings of fixed integration of the dot product operation [32], realizing the organic fusion of the multi-modal features. Therefore, the performance of the grade model reached its optimal level. Exp 4 in (Figure 6d) displayed the final SHAP value sorting, determined by the orchid quality grading model. Compared with the dot product operation, the feature importance sorting of the concatenation operation was unchanged, but the difference of seven SHAP values were more significant. This transformation showed that concatenation operation not only improved model's accuracy, but also further optimized its ability to assess the importance of features.