## 4 AI-Supported Pollen Analysis  
Pollen analysis remains one of the key tools in verifying honey authenticity, as it allows for the precise determination of botanical and geographical origin. This technique involves identifying pollen grains present in honey samples and matching them to plant taxa characteristic of specific regions [56; 62]. Despite its high diagnostic value and widespread application in scientific research and quality audits, classical pollen analysis has significant limitations, including being time-consuming, subjective, and highly dependent on the operator's experience [41]. In response to these challenges, automated tools for pollen analysis based on artificial intelligence (AI) have been developed. In particular, deep learning and CNNs have proven effective for the automatic identification, segmentation, and classification of pollen grains in microscopic images. These systems can process large datasets with high repeatability and accuracy, often exceeding 90%, making them an attractive solution for both industrial and research applications, especially in the context of verifying the authenticity of premium honeys [59; 63; 64; 65]. In automated pollen analysis, a critical step is the precise segmentation of pollen grains from the image background, typically performed using architectures like UNet. This is followed by the extraction of key features, including morphometric (shape, size, contour), textural, and pixel intensity characteristics, which are then used to develop classifiers capable of accurately assigning samples to specific botanical taxa [66; 67]. This approach reduces subjectivity and minimizes interpretative errors, which are particularly important for honeys from regions with high biodiversity, where the complexity of pollen profiles can pose a challenge for traditional methods [64]. Increasingly, the integration of image analysis with spectroscopic techniques, such as FTIR and NIR, is gaining importance, as it enables the differentiation of samples based on their chemical composition and optical properties [67]. Hybrid models combining visual and spectroscopic features show significantly higher classification accuracy, making them a promising solution for validating the authenticity of honeys from different geographical regions and with diverse botanical compositions [68]. AI-supported automated pollen analysis not only improves the efficiency of the analytical process but can also be integrated with traceability systems and analytical platforms, allowing for the automated detection of discrepancies between the declared origin and the actual pollen profile of the product [11; 65; 69]. The use of deep learning algorithms, such as CNNs and transfer learning, enables dynamic model adaptation to new data, which is essential in the context of changing botanical diversity and regional differences. This makes it possible not only to rapidly and accurately process images but also to detect anomalies and identify potential fraud that might be missed by traditional methods. This approach supports VACCP, FSSC 22000, and BRCGS systems, contributing to the operational resilience of supply chains and strengthening consumer confidence in authentic honey products [18; 26; 65]. The application of AI in pollen analysis represents a significant advancement in the detection and prevention of honey adulteration. Deep learning models enable the identification of hidden patterns and subtle anomalies that can indicate potential inconsistencies, thus improving the quality of data used in audits, certifications, and quality management decisions. In light of these advancements, it was essential to situate our proposed system within the context of existing CNN-based pollen classification frameworks to clarify its specific contributions and enhance its practical relevance. In recent years, several peer-reviewed models have been developed:  
* PollenNet achieved approximately 98% accuracy, precision, recall, and F1-score, utilizing semantic segmentation combined with explainable AI techniques [19].