CitrusGS: 3D Gaussian splatting for sparse-view CT reconstruction and precise morphological phenotyping of citrus fruit

Abstract

Computed tomography enables non-destructive phenotyping of fruit internal structure but traditionally requires hundreds of projections, limiting throughput. Under sparse-view conditions, conventional and learning-based methods both suffer from streaking artifacts and regional distortions that degrade trait quantification. This study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting. Our method employs sparse-point initialization, optimized loss composite, and dual-stage pruning to suppress artifacts while preserving anatomically critical details with significantly higer convergence efficiency. In the citrus fruit datasets, CitrusGS achieves 29.78 dB PSNR and 0.870 SSIM, outperforming corresponding baseline method by 1.58 dB and 0.067 in SSIM, and enables automated extraction of ten external and internal phenotypic traits with R 2 larger than 0.944. Moreover, the framework shows initial zero-shot transferability across pathological citrus samples and additional horticultural specimens without retraining. By reconciling acquisition efficiency with anatomical fidelity using low-cost X-ray hardware, CitrusGS provides a promising framework for high-throughput, non-destructive phenotyping in breeding and grading applications.

Plant phenotyping relevance

柑橘果実の疎視野CT再構成法を開発・検証し、内部・外部形質を自動抽出するフェノタイピングが中心である。

Code and data availability

保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。