## 7 Summary and Conclusions  
This study presents a novel digital system for the assessment of honey botanical authenticity, integrating image-based pathological analysis with deep learning techniques and modular IT infrastructure. The solution was developed and validated as part of the NUTRITECH-I-004A/22 project, addressing critical gaps in traditional verification methods,which rely heavily on documentation and physicochemical tests and often fail to detect complex cases of honey adulteration. The interdisciplinary system combines microscopic pollen imaging, automated feature extraction using Hjorth descriptors, and classification via CNNs. Supported by a scalable microservices platform, the system enables real-time image processing and delivers classification accuracy exceeding 90% under controlled laboratory conditions. These findings confirm the feasibility of integrating AI-supported pathological analysis into industrial food quality control workflows. At the same time, this study highlights several methodological and operational challenges. The current dataset, while diverse, does not yet capture the full global variability of honey pollen. The model's robustness outside the development environment remains to be demonstrated, and future work must address integration with industrial systems, regulatory frameworks, and diverse imaging conditions. Nevertheless, the system provides a solid foundation for future advancements in automated food authenticity assessment. These findings allow for several broader conclusions that underline the contribution of this work to the field of food authenticity assessment:  
1. Standardization of data and analytical protocols is fundamental to reproducibility in AI-based food authentication systems. Consistent results require harmonized imaging, annotation, and preprocessing procedures. This study shows that variability in input data quality significantly affects model performance, underlining the need for unified protocols across laboratories and applications. 2. Deep learning applied to microscopic pollen images enables accurate and scalable verification of botanical origin. The tested CNN models achieved high classification accuracy while reducing analysis time and operator dependence. This confirms their utility as a viable alternative to manual palynology, especially in high-throughput or industrial settings. 3. Open and validated reference datasets are essential for model generalization and benchmarking. The effectiveness of AI models depends on access to diverse and standardized training data. Structured databases ensure transferability across regions and products, facilitating regulatory acceptance and scientific transparency. 4. Systemic integration of AI, palynology, and quality management creates robust tools for honey authenticity assessment. The interdisciplinary approach adopted in this project supports the development of scalable, secure, and auditable systems aligned with food safety standards and fraud risk prevention frameworks. Further development efforts, grounded in the findings of this study, will focus on the following:  
Extending the pollen image database to cover additional botanical groups and under-represented geographical regions;  
Invorporating multimodal data (e.g., spectroscopic profiles) into hybrid classification models;  
Designing sector-specific digital tools for VACCP-based risk assessment and fraud prevention;  
Publishing technical validation guidelines to support implementation by laboratories, certification bodies, and industry auditors. This work contributes to the scientific and technological advancement of food authentication systems and supports broader efforts in digital transformation, traceability, and consumer protection within the agri-food sector. Conceptualization, J.K.B. and P.R., methodology, P.R. and B.L. ; formal analysis, J.K.B., P.R. and B.L. ; investigation, P.R. and B.L. ; data curation, J.K.B., P.R. and B.L. ; writing--original draft preparation, J.K.B., P.R. and B.L. ; writing--review and editing, J.K.B. and P.R.