The increasing global population necessitates efficient monitoring of wheat fields to ensure food security. Traditional manual methods for crop monitoring are labor-intensive and inefficient, hindering the adoption of precision agriculture techniques. Deep learning-based semantic segmentation has shown promise in tasks such as yield prediction, crop health monitoring, and disease detection. However, the creation of large-scale annotated datasets required for training these models is resource-intensive. This paper proposes a self-supervised approach for wheat head segmentation, leveraging Lindenmayer systems (L-systems) to generate a synthetic dataset and employing a student-teacher model for domain adaptation. The methodology involves training a semantic segmentation model on synthetic data and refining it with pseudo-labeled real data. Our results demonstrate significant improvements in segmentation performance, achieving a Dice score of 0.87, setting a new benchmark in wheat head segmentation without requiring manual annotation.