  Segment Anything Model (SAM) is a new foundation model that can be used as a
zero-shot object segmentation method with the use of either guide prompts such
as bounding boxes, polygons, or points. Alternatively, additional post
processing steps can be used to identify objects of interest after segmenting
everything in an image. Here we present a method using segment anything
together with a series of post processing steps to segment potato leaves,
called Leaf Only SAM. The advantage of this proposed method is that it does not
require any training data to produce its results so has many applications
across the field of plant phenotyping where there is limited high quality
annotated data available. We compare the performance of Leaf Only SAM to a Mask
R-CNN model which has been fine-tuned on our small novel potato leaf dataset.
On the evaluation dataset, Leaf Only SAM finds an average recall of 63.2 and an
average precision of 60.3, compared to recall of 78.7 and precision of 74.7 for
Mask R-CNN. Leaf Only SAM does not perform better than the fine-tuned Mask
R-CNN model on our data, but the SAM based model does not require any extra
training or annotation of our new dataset. This shows there is potential to use
SAM as a zero-shot classifier with the addition of post processing steps.
