## 1 Introduction  
Citrus is one of the most widely consumed fruits globally and is favored by consumers due to its health benefits and nutritional value. According to the Food and Agriculture Organization of the United Nations (FAO) report, global citrus production reached 76,292,600 tons in 2019, while citrus processing amounted to 16,939,000 tons [1, 2]. Tangerine peel, a by-product of citrus processing, is a valuable Chinese herbal medicine with broad application prospects owing to its medicinal properties. However, the high moisture content (75-90%) of tangerine peel limits its storage duration, creating opportunities for microbial spoilage, fermentation, and chemical degradation within a short period [3]. Drying is a pivotal process in food processing, agricultural preservation, and biopharmaceutical manufacturing, where precise control directly affects product quality and energy efficiency [4]. For instance, in tangerine peel drying, traditional heat pump drying equipment exhibits a dynamic and nonlinear process. Uneven airflow distribution may lead to local over-drying or under-drying, resulting in the loss of active ingredients and diminished nutritional quality [5]. Traditional contact moisture measurement methods, such as gravimetric analysis, offer high accuracy but suffer from drawbacks including poor real-time performance and sample destruction [6]. Artificial intelligence (AI) technologies can address these control challenges by optimizing the drying process, establishing robust predictive models, and ultimately yielding high-quality products [7]. Consequently, researchers have proposed moisture prediction models based on image analysis and environmental parameters to validate the feasibility of non-contact methods [8]. Nevertheless, although non-contact detection techniques such as near-infrared spectroscopy [9] and computer vision [7; 10] have been applied, among these, near-infrared spectroscopy stands out due to its ability to characterize the responses of C-H, O-H, and N-H groups in organic molecules, enabling rapid analysis of material structure, composition, and concentration [9]. However, single-modal data are often insufficient to fully capture the spatiotemporal dynamics of the drying process. In recent years, machine learning techniques have demonstrated unique advantages in drying modeling and dynamic control through the integration of sensor data and multi-objective optimization algorithms [11]. For instance, [PERSON] improved tea moisture prediction accuracy to 92.5% by combining near-infrared spectroscopy with environmental parameters, although dynamic flow field information was not included [9]. These methods provide new strategies to overcome limitations such as the inadequate accuracy of traditional contact measurements and quality fluctuations caused by uneven airflow. Long short-term memory networks (LSTM) employ gating mechanisms that selectively retain or discard information from the cell state, enabling the effective simulation of complex drying dynamics [12]. Existing methods, including LSTM and variants of LSTM, have been applied to predict moisture content, but most approaches lack integration with dynamic environmental data, such as airflow distribution, leading to limitations in capturing the complex spatiotemporal patterns in the drying process [13; 14]. Bidirectional LSTM (BiLSTM) architecture demonstrates exceptional capability in modeling long-range temporal dependencies within sequential data, as evidenced by [PERSON]'s innovative integration of attention mechanisms with BiLSTM networks, which achieved notable improvement in trend pattern recognition for oil production forecasting in well operations [15]. Convolutional neural networks (CNN) have also excelled in image feature extraction; for example, [PERSON] employed a visual geometry group U-shaped network (VGG-UNet) for yarn curling detection, successfully extracting structural features and providing methodological support for visual-driven drying monitoring [16].