## 5 Conclusions  
Emerging technologies, including drones and artificial intelligence, are profoundly transforming precision agriculture by offering innovative tools for crop monitoring and the optimization of agronomic practices. In this study, we demonstrated how the integration of multi-spectral images acquired from drones and satellites with machine learning algorithms can provide detailed information on the health and productivity of basil in an open field, enabling targeted and timely action.  
The results show that advanced image analysis can identify spatial variations in crop conditions with high precision. The use of predictive models based on machine learning techniques has made it possible to correlate vegetation indices with key agronomic parameters, such as productive yield. In particular, the two models which have proven to be the most efficient are Random Forest and k-Nearest Neighbour. These models could be practically applied by farmers and agronomists through user-friendly decision support systems that integrate aerial imagery and provide real-time recommendations on irrigation, fertilization, or harvesting timing, based on predicted crop performance.  
The large-scale implementation of these technologies, however, requires further studies to refine the feature selection process and model calibration. The analysis was conducted on a single location and over one crop cycle, which may restrict the generalizability of the results. Furthermore, the small dataset increased the risk of overfitting and poor model stability. As for the bias in model validation, the authors mitigated the effect by applying leave-one-out cross-validation. Future work should focus on expanding the dataset across multiple sites, seasons, and herbs, and on developing a standardized protocol for ad hoc data collection to improve model training and reproducibility. It could be very useful for farmers to manage a time series of aerial imagery during the production season. This choice would allow us to constantly monitor crop status and adjust the agronomic practice.  
In conclusion, the integration of multispectral imagery and artificial intelligence into agriculture represents a strategic opportunity to increase crop sustainability and productivity. The evolution of these technologies and their diffusion among farmers will contribute significantly to the transition towards increasingly intelligent, resilient and precision-managed agriculture.  
Conceptualization, L.C. and A.M.; methodology, L.C. and N.L.P.; software, L.C. and N.L.P.; validation, M.P. and A.M.; formal analysis, L.C. and N.L.P.; investigation, L.C.; resources, L.C.; data curation, L.C. and A.M.; writing--original draft preparation, L.C.; writing--review and editing, L.C., N.L.P., M.P. and A.M.; visualization, L.C.; supervision, M.P., A.M. and E.F.P.; project administration, E.F.P.; funding acquisition, E.F.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by PSR Marche 2014/2020, Misura 16.1--Sostegno alla creazione e al funzionamento di Gruppi Operativi del PEI, Azione 2 \"Finanziamento dei Gruppi Operativi\". Grant number: 59734, SOSTENIBILI TECH G.O.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
**Acknowledgments:** Authors thank the organic farm \"Il Colle delle Spighe\" for hosting and [PERSON] for helping in field operations.  
**Conflicts of Interest:** The authors declare no conflicts of interest.