Deep learning based detection of sorghum panicles has been proposed to replace manual counting in field trials. However, model performance is highly sensitive to domain shift between training datasets associated with differences in genotypes, field conditions, and various lighting conditions. As labelling such datasets is expensive and laborious, we propose a pipeline of Contrastive Unpaired Translation (CUT) based domain adaptation method to improve detection performance in new datasets, including for completely different crop species. Firstly, original dataset is translated to other styles using CUT trained on unlabelled datasets from other domains. Then labels are corrected after synthesis of the new domain dataset. Finally, detectors are retrained on the synthesized dataset. Experiments show that, in case of sorghum panicles, the accuracy of the models when trained with synthetic images improve by fifteen to twenty percent. Furthermore, the models are more robust towards change in prediction thresholds. Hence, demonstrating the effectiveness of the pipeline.