Parcel delineation represents a fundamental challenge in agricultural management. It plays a pivotal role in multiple domains, including agricultural policy implementation, crop yield estimation, and environmental monitoring. Modern Remote Sensing Images (RSIs) offer a revolutionary approach to parcel boundary identification, providing high-resolution, comprehensive spatial data that can capture intricate landscape details across vast agricultural regions. Advanced image processing techniques, particularly deep learning and machine learning algorithms, have dramatically enhanced the capability to automatically extract and define field boundaries with remarkable accuracy. In addition, state-of-the-art (SOTA) methods leverage sophisticated classification and segmentation techniques to precisely define agricultural boundaries from satellite imagery, addressing the complex challenge of accurately delineating Cultivated Land Parcels (CLPs). Traditional segmentation approaches, including edge-based, region-based, and hybrid methods, have long been fundamental in agricultural field mapping, each offering distinct methodological strategies for identifying and demarcating land parcels. The rapid advancement of deep learning techniques has revolutionized RSI analysis, particularly in complex agricultural domain applications. In this study, we introduce an innovative two-stage hybrid method for agricultural parcel delineation, leveraging a novel FracTAL UNet Attention Residual Refinement Module (FracTAL UNet ARRM). Our proposed method represents a significant leap forward in the SOTA. Comprehensive performance evaluation was conducted using multiple metrics, including accuracy, F1-score, mean intersection over union (mIoU), and Boundary Displacement Error (BDE). The FracTAL UNet ARRM method we developed outperforms SOTA methods, achieving the best performances in terms of accuracy, F1-score, mIoU, and BDE, respectively.