Analyzing tree morphology, particularly trunk and branch extraction, is valuable for genetic breeding and forestry management. Existing image-based deep learning methods tend to misidentify overlapping trunks as a single trunk when structural discontinuities occur due to front-back overlap, while low contrast between trunk textures and the background further complicates segmentation. Moreover, limited juvenile tree data, coupled with substantial variations in trunk diameter across growth stages, restricts model performance in extracting thin trunks and branches. Based on this, we propose an instance segmentation network leveraging frequency-domain features, WaveInst. Its core is a Frequency-domain Feature Compensation branch, consisting of Discrete Wavelet Transform block and High-Frequency Enhancement block. The former performs high- and low-frequency decomposition and aggregates high-frequency responses along multiple directions, while the latter further refines the high-frequency features through multi-path processing. An Adaptive Gated Fusion Module is then applied to effectively integrate spatial-domain convolutional features with frequency-domain representations, allowing the decoder to utilize embedded frequency-domain features to enhance its fine-grained detail representation. We conduct experiments on public datasets including SynthTree43k, CaneTree100, and UrbanStreet, as well as PoplarDataset, which contains both mature and juvenile poplar trees. On the public datasets, WaveInst demonstrates strong performance and stable robustness across diverse scenarios. On PoplarDataset, it achieves a mean average precision of 53.1 for mature and 29.1 for juvenile, outperforming existing state-of-the-art methods on juvenile by 6.6 points, demonstrating its effectiveness in extracting thin tree trunk.

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