Image segmentation plays a key role in many domains, particularly in precision agriculture, where accurate classification of crops and vegetation is essential for optimizing farming practices. Multispectral imaging enhances segmentation performance by providing spectral information beyond the visible range, enabling a better performance in real-world conditions. Deep learning models have shown remarkable success in multispectral image segmentation however, the predictions often lack spatial coherence thus leading to noisy segmentation outputs that can negatively impact agricultural decision-making. To address this challenge, we propose a spatial dense Conditional Random Field (CRF) framework for post-processing deep learning-based segmentation. Instead of relying solely on sigmoid activation functions for thresholding logits, our approach refines segmentation outputs by leveraging spatial dependencies between pixels. This method involves estimating the CRF parameters based on training data and applying the refined model on test-logits and consequently enhancing segmentation performance. By integrating spatial information the proposed framework improves the consistency and robustness of segmentation results, ultimately leading to better decision-making in precision agriculture.