## 5 Discussion  
In this study, a high-precision prediction model for tangerine peel moisture ratio was developed through multimodal data fusion and deep learning techniques, yielding promising results in controlled laboratory settings. However, for real-world deployment and industrial-scale application, several technical and practical aspects warrant further discussion. Although the model achieved strong predictive performance, we observed that the prediction errors were slightly higher at the initial and final stages of drying--corresponding to high and low moisture values, respectively. These deviations are mainly attributed to rapid morphological changes in the early drying phase and lower contrast or deformation in the  
Figure 18: Comparison of measured and predicted moisture ratios. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Moisture Ratio/\%**} & \multirow{2}{*}{**Error/\%**} & \multirow{2}{*}{**Relative Error/\%**} \\ \cline{2-2} \cline{4-5}  & **Predicted Value/\%** & & & \\ \hline a & 78.4 & 77.6 & 0.8 & 1.03 \\ b & 62.3 & 61.8 & 0.5 & 0.81 \\ c & 51.6 & 52.5 & 0.9 & 1.71 \\ d & 38.4 & 38.2 & 0.2 & 0.52 \\ e & 10.5 & 10.9 & 0.4 & 3.67 \\ \hline \end{tabular}
\end{table} 
Table 10: Accuracy test results of the KAN-BiLSTM prediction model. late stage, which affect the extraction of visual and spatial features. Future refinements may include temporal segmentation strategies or dynamic feature recalibration to address such transitions. Compared to traditional single-modal models, such as LSTM or CNN-based methods using only mass loss or image features, the proposed KAN-BiLSTM architecture integrates both image-derived features and simulated airflow data to enhance nonlinear learning and temporal reasoning. Prior studies in food drying, such as those by [PERSON] and [PERSON], reported R\({}^{2}\) values around 0.82-0.88 using conventional LSTM-based models [14; 22]. In contrast, our model achieved R\({}^{2}\) = 0.9908 and MAE = 0.024, highlighting its substantial improvements in accuracy and robustness, particularly in capturing spatial heterogeneity and subtle moisture variations. The multimodal data fusion strategy successfully overcomes the limitations of single-modal approaches in monitoring drying dynamics. This not only enhances segmentation and prediction accuracy but also provides a scientific basis for parameter optimization and process control in drying equipments. Second, by employing the KAN layer as a feature extraction module, the mapping capacity for high-dimensional nonlinear features is significantly enhanced through adaptive nonlinear activation functions. Coupled with the BiLSTM's ability to capture bidirectional temporal dependencies and the MHA module's focus on key time steps and features, the proposed method offers a novel approach for precise control of the drying process for tangerine peel and other agricultural products. In addition to its technical advantages, the model has promising industrial implications. By enabling accurate and real-time moisture prediction, it may reduce energy consumption by preventing over-drying, minimize labor reliance through non-contact monitoring, and support process automation in heat pump drying systems. Furthermore, this multimodal modeling framework is adaptable to other agricultural products with similar surface-texture dynamics, demonstrating good scalability. The ability to monitor drying conditions visually and spatially provides a foundation for intelligent drying control. It should be noted that the current experiments were conducted under a single drying condition (45 \({}^{\circ}\)C and 0.5 m/s airflow), which may limit the model's generalizability to broader scenarios with varying temperature and humidity settings. While the model structure supports expansion to multivariate conditions, further data collection under diverse drying regimes will be required to evaluate its robustness and adaptability.