## 1 Introduction  
Precision agriculture refers to the use of advanced technologies, including GPS guidance, control systems, sensors, robotics, drones, autonomous vehicles, and variable rate technology, to optimize farm management. It aims to reduce costs and increase yields in farming while ensuring sustainability and environmental protection [1]. Deep learning (DL) methodologies can offer efficient, automated, and data-driven decision-making in agricultural practices. DL has demonstrated significant advancements in visual data analysis across various tasks, including image classification [2, 3], object detection [4, 5], semantic segmentation [6, 7, 8], and instance segmentation [9, 10, 11]. Automated visual monitoring of agricultural fields can aid in the early identification of issues such as pest infestations, diseases, and nutrient deficiencies while optimizing resource utilization. Consequently, this approach facilitates timely interventions, which can lead to enhanced crop yields, improved quality of harvests, and increased overall operational efficiency, thereby ensuring sustainability. However, the wide adoption of DL approaches for crop monitoring faces significant challenges. Agricultural fields are constantly changing environments. For example, a crop field in the early growth stages is substantially different from the same field in the later stages of growth. Additionally, these systems must operate accurately under various weather and lighting conditions. This challenges the generalizability of DL models because models trained on data from a specific growth stage of a field might not generalize well to the same crop at different growth stages. In the DL context, this phenomenon is known as a distribution shift [12; 13]. One could develop a large-scale dataset encompassing various crop growth stages and environmental conditions to alleviate this issue. However, this approach presents challenges to data collection and annotation. Collecting data from various growth stages of crop fields under various weather and lighting conditions is time-consuming. Further, annotating agricultural images is particularly challenging, often requiring pixel-level annotation. These images frequently contain numerous objects of interest--e.g., wheat spikes in a wheat field--making the data annotation process laborious. Environmental conditions--such as climate, soil quality, geographical location, and pest and disease pressure--significantly contribute to data variation in crop datasets. Variations in temperature, precipitation, humidity, soil types, regional microclimates, and pest and disease prevalence collectively influence crop growth and yield outcomes. Further, phenotypic varieties introduce considerable data variation into crop datasets, attributed to genetic diversity, breeding practices, and different responses to environmental stresses. The unique traits of various genetic varieties, including their drought and pest resistance, yield potential, and nutritional content, and the specific characteristics of hybrid and traditional crops, significantly influence growth and development patterns. In addition, growth stages contribute to data variation in crop datasets, with data collected at various points in the crop lifecycle, from germination to harvesting phases, being substantially different. As such, deep learning models developed only using datasets generated from one snapshot of crop fields might not generalize well when applied to other fields with different environmental, phenotypic, and growth stages. Therefore, there is a pressing need to develop automated or semi-automated methodologies that enhance the performance of deep learning models on new datasets without requiring substantial manual annotation. Domain adaptation techniques refer to the methodologies used to alleviate distribution shifts and can be divided into supervised, semi-supervised, self-supervised, and unsupervised approaches depending on whether or not annotated data are used for domain adaptation [14; 15].