Rapid and accurate tree crown detection is significant to forestry management and precision forestry. However, the variability of the data is challenging for traditional deep learning-based methods in cross-regional scenarios. To solve the problem of low accuracy of existing methods due to the distribution characteristics of tree crowns and the special background in which they are located, we propose a multi-adversarial domain adaptive (DA) crown detection model, which saves much of labeling efforts needed for covering different features in different regions. First, we embed a multilevel transferable attention mechanism to extract the multiscale transferable features of the tree crown and combat the negative transfer caused by the background. Second, a multi-adversarial instance alignment module with entropy regularization and a weighting factor is designed to match the cross-regional data distributions to reduce the misdetection and misidentification of complex backgrounds. The above-mentioned adaptations are integrated into a two-stage object detector to realize end-to-end training. We conduct comprehensive experiments with four transfer tasks. Our method improves F1-score to 95.68%, 83.12%, 78.56%, and 66.89% and performs 3.61% and 15.38% better than the state-of-the-art domain adaptation approaches without extra inference costs. The results demonstrate the great potential of our method for DA tree crown detection, which provides a reference for further research on forestry detection.