SMFCA-Net: Sparse Multifrequency Cross-Attention Network for Single-Plant Point Cloud Classification and Segmentation

Three-dimensional point clouds have become a cornerstone of single-plant phenotyping, with deep learning serving as the main driver of high-throughput trait analysis. However, repeated downsampling operations in U-shaped and pyramidal architectures have been shown to oversmooth features and erode fine-grained details in 3D single-plant point clouds. To address this, the present study proposes SMFCA-Net, a frequency-aware framework integrating a Multi-Frequency Fourier Cross-Attention (MFCA) module into the sparse U-Net bottleneck. The MFCA bottleneck, a combination of spectral partitioning and cross-band attention, has been shown to capture both long-range contextual dependencies and local structures. This achieves a better balance between global and local information. The MFCA module comprises a three-stage pipeline: The first stage is frequency decomposition (FD), which uses a 3D fast Fourier transform (3D FFT) to separate features into low-, mid-, and high-frequency bands. The second is frequency-aware cross-attention (CA), which facilitates cross-band interactions anchored on mid-frequency semantics. The third and final stage is cross-frequency adaptive fusion (AF), which aggregates the enhanced bands using learnable weights. Extensive experimentation on a range of crops and acquisition scenarios has demonstrated consistent improvements in single-plant classification, semantic segmentation, and instance segmentation. This highlights the effectiveness of SMFCA-Net for high-throughput phenotyping. The source code can be accessed at the following URL: https: //github.com/yangxin6/SMFCA.git.