Plant 3D reconstruction plays a critical role in precision agriculture and plant growth monitoring, yet it faces challenges such as complex background interference, difficulties in capturing intricate plant structures, and a slow reconstruction speed. In this study, we propose PlantSamGaussianReconstruction (PSGR), a novel method that integrates Grounding SAM with 3D Gaussian Splatting (3DGS) techniques. PSGR employs Grounding DINO and SAM for accurate plant–background segmentation, utilizes algorithms such as Scale-Invariant Feature Transform (SIFT) for camera pose estimation and sparse point cloud generation, and leverages 3DGS for plant reconstruction. Furthermore, a 3D–2D projection-guided optimization strategy is introduced to enhance segmentation precision. The experimental results of various multi-view plant image datasets demonstrate that PSGR effectively removes background noise under diverse environments, accurately captures plant details, and achieves peak signal-to-noise ratio (PSNR) values exceeding 30 in most scenarios, outperforming the original 3DGS approach. Moreover, PSGR reduces training time by up to 26.9%, significantly improving reconstruction efficiency. These results suggest that PSGR is an efficient, scalable, and high-precision solution for plant modeling.

Dynamic gesture detection is a key topic in computer vision and deep learning, with applications in human–computer interaction and virtual reality. However, traditional methods struggle with long sequences, complex scenes, and multimodal data, facing issues such as high computational cost and background noise. This study proposes an Attention-Enhanced dual-layer LSTM (Long Short-Term Memory) network combined with Grounding SAM (Grounding Segment Anything Model) for gesture detection. The dual-layer LSTM captures long-term temporal dependencies, while a multi-head attention mechanism improves the extraction of global spatiotemporal features. Grounding SAM, composed of Grounding DINO for object localization and SAM (Segment Anything Model) for image segmentation, is employed during preprocessing to precisely extract gesture regions and remove background noise. This enhances feature quality and reduces interference during training. Experiments show that the proposed method achieves 96.3% accuracy on a self-constructed dataset and 96.1% on the SHREC 2017 dataset, outperforming several baseline methods by an average of 4.6 percentage points. It also demonstrates strong robustness under complex and dynamic conditions. This approach provides a reliable and efficient solution for future dynamic gesture-recognition systems.