## 1 Introduction
However, their application in drying has mostly been limited to offline image analysis, lacking real-time fusion with dynamic environmental parameters. Computational fluid dynamics (CFD) simulations can accurately model fluid flow, heat transfer, and mass transfer within drying chambers, revealing spatial heterogeneity in air velocity and temperature that drives uneven drying [17]. [PERSON] used CFD to simulate the flow field within a drying chamber and optimize its configuration, thereby significantly improving drying uniformity [18]. Yet, to date, CFD outputs have seldom been fused with machine learning frameworks, leaving a gap between physical simulation data and data-driven prediction models. The Kolmogorov-Arnold network (KAN) enhances nonlinear representation by replacing traditional linear weights with B-spline-based activation functions, thereby offering greater flexibility in modeling high-dimensional, strongly nonlinear relationshipscharacteristics typical of coupled airflow-material interactions during drying [4]. For instance, [PERSON] applied KAN to hyperspectral image classification by integrating it with CNN, resulting in a 33.58% reduction in background reconstruction error during hyperspectral anomaly detection and achieving an accuracy improvement of 3.88% over traditional multilayer perceptron (MLP) methods [19]. Similarly, enhanced CNN architecture via KAN attained 98.38% accuracy in gesture recognition tasks [20]. While KAN has shown success in domains such as hyperspectral image classification and gesture recognition, its application to drying kinetics modeling is unprecedented; here, its capacity to learn complex spatiotemporal dynamics is particularly advantageous. To address these challenges, we propose a novel multimodal fusion strategy that combines CFD-derived airflow and temperature fields with image-based features for moisture prediction. This study hypothesizes that combining visual features with airflow simulation data in a multimodal framework will significantly improve the accuracy of moisture prediction during drying. Central to our approach is the integration of a KAN with a BiLSTM. A semantic segmentation model based on VGG-UNet extracts microscopic features, such as tangerine peel color and texture [16], while finite element analysis (FEA) quantifies the spatial heterogeneity of airflow velocity and temperature distribution inside the drying chamber. This fusion strategy overcomes the limitations of traditional single-modal models. Ultimately, this model provides a valuable reference for the design of efficient drying processes. Therefore, this study aims to propose a Kolmogorov-Arnold network bidirectional long short-term memory (KAN-BiLSTM) neural network model to enhance input feature representation and develop a more accurate predictive model for the tangerine peel moisture ratio. The main contributions and innovations of this study are summarized as follows:  
* Multimodal dataset construction: A comprehensive dataset was developed by synchronously collecting mass loss data and high-resolution images of tangerine peel under controlled drying conditions. This enabled detailed spatiotemporal analysis of drying kinetics and morphological evolution;
* Airflow distribution analysis: In traditional heat pump drying, uneven airflow distribution in the drying chamber results in varying drying rates and uneven moisture content. Finite element analysis was employed to simulate airflow distribution and quantify its impact on drying performance, thereby providing insights into addressing uneven drying;
* Enhanced image segmentation: By integrating a pre-trained VGG encoder into the UNet architecture, the segmentation model achieved superior accuracy and robustness, enabling precise extraction of peel contours under complex backgrounds;
* Novel moisture prediction model: A novel KAN-BiLSTM model was proposed to predict moisture ratio by fusing image features and temperature data.