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.

本研究通过综合探索使用State-of-the-Art (SOTA)小麦作物监测模型进行跨地区和跨年份分布外泛化的可行性，旨在探讨Parameter Efficient Fine Tuning (PEFT)方法在作物监测中的应用。具体而言，我们致力于适应SOTA TSViT模型以解决冬小麦田地分割问题，这是作物监测和食品安全的关键任务。通过使用PEFT技术，我们只训练了TSViT架构的仅0.7%的参数，并实现了与完全微调方法相媲美的显著结果。我们拥有的Beqaa-Lebanon数据集包含了五年时间内总面积为170平方公里的小麦和非小麦类别的高质量标注多边形。利用Sentinel-2图像，我们的模型达到了84%的F1分数。我们计划公开发布黎巴嫩冬小麦数据集、代码库和模型权重。

冬小麦分割的PEFT技术实证研究