Comparing the Segment Anything Model with Region Growing Algorithms in the detection of irrigated croplands

The advance of remote sensing and geotechnologies has helped to solve agricultural-related problems, especiallythose connected to management practices such as irrigation. Image segmentation techniques, for example, bring thepossibility of identifying areas and borders of irrigated croplands, a factor that can enhance area and yield estimates. In thisresearch area, a recent innovation is the Segment Anything Model (SAM) algorithm. Thus, this study aimed to compareSAM with two well-known remote sensing image segmentation algorithms, Region Growing and Baatz-Schape, to delineateirrigated agricultural lands in the Brazilian semiarid region. The findings indicate that SAM has the potential to generatehomogeneous segments when examining such lands. However, it requires refinements in order to distinguish fields withvarying crops.