  Climate change is increasingly disrupting worldwide agriculture, making
global food production less reliable. To tackle the growing challenges in
feeding the planet, cutting-edge management strategies, such as precision
agriculture, empower farmers and decision-makers with rich and actionable
information to increase the efficiency and sustainability of their farming
practices. Crop-type maps are key information for decision-support tools but
are challenging and costly to generate. We investigate the capabilities of Meta
AI's Segment Anything Model (SAM) for crop-map prediction task, acknowledging
its recent successes at zero-shot image segmentation. However, SAM being
limited to up-to 3 channel inputs and its zero-shot usage being class-agnostic
in nature pose unique challenges in using it directly for crop-type mapping. We
propose using clustering consensus metrics to assess SAM's zero-shot
performance in segmenting satellite imagery and producing crop-type maps.
Although direct crop-type mapping is challenging using SAM in zero-shot
setting, experiments reveal SAM's potential for swiftly and accurately
outlining fields in satellite images, serving as a foundation for subsequent
crop classification. This paper attempts to highlight a use-case of
state-of-the-art image segmentation models like SAM for crop-type mapping and
related specific needs of the agriculture industry, offering a potential avenue
for automatic, efficient, and cost-effective data products for precision
agriculture practices.
