## 5 Conclusions  
A multi-modal Transformer for _Rchb. f._ quality grading combined with explainable features representation was firstly developed. A dataset of 4556 _Rchb. f._ samples was obtained from the Orchid Trading Website. Seven key features were extracted through regular expression matching. Four strategies were proposed for excellent accuracy. Firstly, the Transformer Encoder adapted missing features, and its self-attention dynamically prioritized important features. The dot product input feature's existence and feature value into the Transformer Encoder simultaneously. Secondly, UNet was employed for petal segmentation, integrating the average RGB of petals into the Transformer Encoder, addressing data sparsity and overfitting in textual data, and enabling the model to grade the orchid quality by color. Thirdly, ViT was utilized to transform petal images into global fine-grained features, which were combined with color extracted from UNet and numerical features. Finally, the concatenation operation helped the model to understand the relationship between key features and values adequately, and an excellent accuracy of 93.13% was achieved. Furthermore, SHAP analysis algorithm ranked feature importance and gave the model explainability for grading results. Therefore, this multi-modal Transformer exhibited its advantage of explainable features representation affecting the _Rchb. f._ value. This study  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Training** \\ **ID** \\ \end{tabular} } & \multirow{2}{*}{**Encoder Input**} & \multicolumn{3}{c}{**Accuracy of Validation Set**} & \multicolumn{3}{c}{**Accuracy of Test Set**} \\ \cline{3-8}  & & **VQ-VAE** & **C-VAE** & **Transformer** & **VQ-VAE** & **C-VAE** & **Transformer** \\  & & **Encoder** & **Encoder** & **Encoder** & **Encoder** & **Encoder** & **Encoder** \\ \hline \#1 & Numerical features & 57.21\% & 51.36\% & 63.02\% & 52.56\% & 48.71\% & 59.36\% \\ \#2 &
\begin{tabular}{c} Numerical features \\ \(\otimes\) RGB \\ feature from ViT \\ \end{tabular} & 75.67\% & 71.32\% & 84.92\% & 72.81\% & 69.10\% & 81.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison of validation set and test set with VQ-VAE Encoder, C-VAE Encoder, and Transformer Encodersuggested an effective way to establish a reliable digital index system for agricultural product quality evaluation without referred standards.  
Conceptualization, Z.W.; methodology, Z.W.; software, Z.W.; validation, X.H.; formal analysis, X.H.; investigation, Y.W.; resources, X.H.; data curation, X.H.; writing--original draft preparation, Z.W. and X.H.; writing--review and editing, X.L.; visualization, Z.W.; supervision, Y.W.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovation Program of Chinese Academy of Agricultural Sciences (Funder No: CAAS-CAE-202302, CAAS-ASTIP-2024-AII). Not applicable. Not applicable. Not applicable. Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.