Weeding is a critical, yet labor intensive task in agriculture, where manual methods are inefficient and unsustainable for large-scale farming. To address this challenge, we propose two complementary components aimed at enhancing precision agriculture: (1) a custom designed autonomous robotic platform for efficient field navigation and monitoring, and (2) a novel unsupervised domain adaptation (UDA) framework for robust crop and weed segmentation. The proposed UDA framework integrates two key modules, the contrastive learning module (CLM) for improved feature alignment and the enhanced fast fourier transform module (EFFT) for stylistic adaptation designed to mitigate domain gaps between source and target datasets. Extensive experiments on diverse agricultural datasets, including UAV-Bonn, UAV-Zurich, Sunflower, and Sugarbeet, demonstrate that the proposed method achieves significant improvements in mean Intersection over Union (mIoU), outperforming the MIC baseline by 15.52% and 13.22% in the Sunflower-to-Sugarbeet and Sugarbeet-to-Sunflower tasks, respectively, and delivering competitive results in UAV-Bonn-to-UAV-Zurich and superior performance in UAV-Zurich-to-UAV-Bonn. These results highlight the system’s potential for advancing sustainable and autonomous weeding in precision agriculture, with future integration of the UDA framework into the robotic platform enabling fully automated weed management.