**5. Conclusions**

The current study explored the sensitivity of thermal image-based orchard water status estimation to canopy extraction quality using four canopy extraction methods, which was previously unaddressed in scientific literature. Three methods used a single thermal image (1-source) (2PE, ED, and VS), while a fourth method incorporated a thermal and an RGB image (multi-source) (RGB-BM). Two approaches to canopy temperature calculation were also evaluated: the average of all canopy pixels (T100%) and the average of the coolest 33% of canopy pixels (T33%). This study found that canopy pixels can be extracted with high accuracy and reliability using only thermal images, primarily using the 2PE and ED methods. The incorporation of an RGB image reduces the overall quality, as between-row weeds and warm canopy edges are misidentified as tree canopy. Additionally, the T33% approach to canopy temperature calculation was found to be robust and not sensitive to canopy extraction accuracy. In comparison, the T100% approach, specifically for the VS and RGB-BM methods, overestimated orchard water stress. These findings indicate that orchard water status is sensitive to canopy extraction quality but is affected to a greater degree by the canopy temperature calculation approach. Future research should explore the relationship between SWP and CWSI on additional days, under different meteorological conditions, and over seasons to strengthen the estimation of orchard water status. Future research should also explore the sensitivity of orchard water status to canopy extraction quality in additional varieties of peach and other fruit trees located in different environments. Such research studies will widen the scope of impact and scale of the main findings from this study, improving irrigation management based on thermal images.

**Author Contributions:** Conceptualization, L.K. and Y.C.; Methodology, L.K. and Y.C.; Software, E.G.; Validation, L.K.; Formal Analysis, L.K. and S.M.; Investigation, L.K., G.L., O.K. and V.A.; Resources, G.L., O.K. and V.A.; Data Curation, L.K., S.M. and E.G.; Writing—Original Draft Preparation, L.K.; Writing—Review and Editing, L.K., Y.C., A.B.-G., M.I.L., A.P. and E.G.; Visualization, L.K.; Supervision Y.C., A.B.-G. and M.I.L.; Project Administration, L.K., A.N. and V.A.; Funding Acquisition, Y.C., A.B.-G. and V.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was a part of the "Eugene Kendel" Project for the Development of Precision Drip Irrigation funded via the Ministry of Agriculture and Rural Development in Israel (Grant No. 20-12-0030). The project also received funding from the European Union's Horizon 2020 research and innovation program under Project SHui, grant agreement No 773903.

**Data Availability Statement:** Data sharing not applicable.

**Acknowledgments:** The authors would like to thank the grower, Shlomo Cohen, for collaborating and allowing the research to be conducted in his peach orchard; Reshef Elmakais, Tomer Hagai, and Ohad Masad, for field measurements and technical support; and Datamap company for imagery acquisition and pre-processing.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.
