## 5 Conclusions  
Many regions in Italy have witnessed a significant increase in hazelnut cultivation, and Tuscia, particularly the province of Viterbo, is no exception. Such rapid expansion has brought about significant social changes, further promoting the industrialization of the sector and property concentration. Small producers are making way for medium to large companies that actively search for new plots. As a result, hazelnut production and marketing are based on well-organized companies, often capable of devising innovative solutions and maintaining strong integration with other players in the supply chain. Figure 21: Ground investigation within the clusters. Photos (**A**,**B**) were taken (by the authors) in June 2023, within Cluster 3 and Cluster 1 patches, respectively. Image (**C**) was captured from Google Earth, in correspondence to a Cluster 2 patch (July 2019). The red box in Image C highlights the presence of the drip irrigation hose. The present study has demonstrated how the wealth of multi-temporal and multi-platform data available through cloud platforms allows for accurate analyses of specific crops. Additionally, consideration must be given to the spatial, temporal, and spectral resolutions of the sensors used. In our study, the implemented workflow has successfully identified 22,780 hectares of hazelnut orchards within the agroindustrial system of Viterbo. This estimation closely aligns with the 21,700/23,900 hectares reported in the national statistical system in the year 2022, primarily concentrated within the PDO-designated area. Our analysis has unveiled three distinct geographic distribution patterns of hazelnut orchards. The historical area is notable on the elevated terrain of the Cimino volcanic system. In tandem, a compact cluster extends along the slopes towards the Tiber River, constituting relatively newer yet well-established hazelnut orchards that likely represent the core of hazelnut production in this region. This sector displays denser planting arrangements, irrigation practices, and a high degree of mechanization. Another cluster signifies nascent plantations characterized by larger parcel systems (>20 hectares), indicating the recent expansion of hazelnut cultivation. Nevertheless, these areas remain confined within the PDO-designated region and do not extend into other PDO production zones, such as PDO Canino olive oil and the renowned vineyards in the Lake Bolsena area. To enhance the methodology's performance, the integration of an additional layer based on high-resolution orthophotos is proposed. This layer would facilitate the extraction of specific textures, particularly relevant for very young hazelnut orchards, addressing a current limitation related to the spatial resolution of available satellites. Simultaneously, this augmentation would enable effective field segmentation through CNNs (Convolutional Neural Networks) or computer vision algorithms, thereby mitigating edge effects. In conclusion, the work described in the presented paper proves to be suitable for the specific mapping of certain perennial tree species in agricultural settings. By leveraging a three-year median for optical data and an average SAR for recent years, this methodology adopts a spectral-separated hierarchical approach. It effectively discriminates a specific perennial crop, allowing to characterize diverse aspects influenced by environmental variations and agronomic practices. Such an approach facilitates a comprehensive regional exploration, holding considerable implications for corporate interests within the realm of Agriculture 4.0. Conceptualization, F.L. and M.P. ; methodology, F.L. and M.P. ; software, F.L. ; validation, F.L. ; formal analysis, F.L. ; investigation, F.L. ; data curation, F.L. ; writing--original draft preparation, F.L. and M.P. ; writing--review and editing, F.L., G.P., M.S., L.V., S.G. and M.P. ; visualization, F.L., G.P., M.S., L.V., S.G. and M.P. ; supervision, G.P., M.S., L.V. and M.P. All authors have read and agreed to the published version of the manuscript.