  Parameter Efficient Fine Tuning (PEFT) techniques have recently experienced
significant growth and have been extensively employed to adapt large vision and
language models to various domains, enabling satisfactory model performance
with minimal computational needs. Despite these advances, more research has yet
to delve into potential PEFT applications in real-life scenarios, particularly
in the critical domains of remote sensing and crop monitoring. The diversity of
climates across different regions and the need for comprehensive large-scale
datasets have posed significant obstacles to accurately identify crop types
across varying geographic locations and changing growing seasons. This study
seeks to bridge this gap by comprehensively exploring the feasibility of
cross-area and cross-year out-of-distribution generalization using the
State-of-the-Art (SOTA) wheat crop monitoring model. The aim of this work is to
explore PEFT approaches for crop monitoring. Specifically, we focus on adapting
the SOTA TSViT model to address winter wheat field segmentation, a critical
task for crop monitoring and food security. This adaptation process involves
integrating different PEFT techniques, including BigFit, LoRA, Adaptformer, and
prompt tuning. Using PEFT techniques, we achieved notable results comparable to
those achieved using full fine-tuning methods while training only a mere 0.7%
parameters of the whole TSViT architecture. The in-house labeled data-set,
referred to as the Beqaa-Lebanon dataset, comprises high-quality annotated
polygons for wheat and non-wheat classes with a total surface of 170 kmsq, over
five consecutive years. Using Sentinel-2 images, our model achieved a 84%
F1-score. We intend to publicly release the Lebanese winter wheat data set,
code repository, and model weights.
