Abstract
Precision agriculture uses accurate identification and mapping of crop features by automated mechanisms. Using computer vision techniques implemented by supervised deep learning systems to solve many precision agricultural problems necessitates large-scale data collection and prolonged ground truth annotation by humans. The so-called foundation models in Artificial Intelligence (AI) are becoming increasingly significant. Meta AI Research is working on a project called Segment Anything to provide a base model for image segmentation. It can accomplish zero-shot generalisation to strange objects and images without additional training. This study evaluates the performance of the Segment Anything Model (SAM) for the problem of semantic segmentation of objects in the context of precision agriculture.
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