Comparative Analysis of Soil Moisture Interpolation Techniques in Apple Orchards of Trentino Region

This paper provides valuable insights into the application of spatial interpolation techniques in smart agriculture and highlights the potential for further improvements through the integration of advanced geostatistical models. Specifically, it evaluates and compares two spatial interpolation techniques, Inverse Distance Weighting and Ordinary Kriging, for estimating soil moisture in apple orchards located in the Val di Non region of Trentino, Italy. Data were gathered from 18 tensiometer sensors deployed across the apple orchards, providing continuous soil moisture measurements over a specified time frame in 2023. The accuracy of both interpolation methods was assessed using root mean square error as the primary evaluation metric, with various validation methods employed to ensure robustness. Additionally, statistical analyses were conducted to determine the significance of differences in performance between the methods. The results indicate that Inverse Distance Weighting, despite its computational efficiency, slightly outperforms Ordinary Kriging in terms of accuracy, with statistically significant lower error values, making it a preferable choice for real-time soil moisture mapping and precision irrigation management in the region