Adapting Vision Foundation Models for Plant Phenotyping

Foundation models are large models pre-trained on
tremendous amount of data. They can be typically adapted
to diverse downstream tasks with minimal effort. However,
as foundation models are usually pre-trained on images or
texts sourced from the Internet, their performance in specialized
domains, such as plant phenotyping, comes into
question. In addition, fully fine-tuning foundation models
is time-consuming and requires high computational power.
This paper investigates the efficient adaptation of foundation
models for plant phenotyping settings and tasks.