Unverified paper record
Seed 3D Phenotyping Across Multiple Crops Using 3D Gaussian Splatting
Agriculture · 8 Nov 2025 · 10.3390/agriculture15222329
Abstract
This study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops—including maize, wheat, and rice—and designed to overcome the inefficiency and subjectivity of manual measurements and the high costs of laser-based phenotyping. A panoramic video of the seed is captured and processed through frame sampling to extract multi-view images. Structure-from-Motion (SFM) is employed for sparse reconstruction and camera pose estimation, while 3D Gaussian Splatting (3DGS) is utilized for high-fidelity dense reconstruction, generating detailed point cloud models. The subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume. The experimental evaluations demonstrated a high measurement accuracy, with coefficients of determination (R2) for length, width, and height reaching 0.9361, 0.8889, and 0.946, respectively. Moreover, the reconstructed models exhibit superior image quality, with peak signal-to-noise ratio (PSNR) values consistently ranging from 35 to 37 dB, underscoring the robustness of 3DGS in preserving fine structural details. Compared to conventional multi-view stereo (MVS) techniques, the proposed method can achieve significantly improved reconstruction accuracy and visual fidelity. The key outcomes of this study confirm that the 3DGS-based pipeline provides a highly accurate, efficient, and scalable solution for digital phenotyping, establishing a robust foundation for its application across diverse crop species.
Plant phenotyping relevance
3DGSを用いた種子の3D再構成・点群処理・形質抽出パイプラインを開発し、精度を評価しており、植物表現型取得手法が研究の中心である。
abstractThis study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops
abstractThe subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume.
abstractThe experimental evaluations demonstrated a high measurement accuracy
Code and data availability
The paper describes seed 3D phenotyping using 3DGS, but no public dataset, image, code, or model repository is provided. The Data Availability Statement only offers further inquiries via the corresponding author, so any paper-specific assets would require contacting the authors.
No evidence-backed public reproduction asset is currently recorded.
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