Reconstruction, Segmentation and Phenotypic Feature Extraction of Oilseed Rape Point Cloud Combining 3D Gaussian Splatting and CKG-PointNet++

Abstract

Phenotypic traits and phenotypic extraction at the seedling stage of oilseed rape play a crucial role in assessing oilseed rape growth, breeding new varieties and estimating yield. Manual phenotyping not only consumes a lot of labor and time costs, but even the measurement process can cause structural damage to oilseed rape plants. Existing crop phenotype acquisition methods have limitations in terms of throughput and accuracy, which are difficult to meet the demands of phenotype analysis. We propose an oilseed rape segmentation and phenotyping measurement method based on 3D Gaussian splatting with improved PointNet++. The CKG-PointNet++ network is designed to integrate CGLU and FastKAN convolutional modules in the SA layer, and introduce MogaBlock and a self-attention mechanism in the FP layer to enhance local and global feature extraction. Experiments show that the method achieves a 97.70% overall accuracy (OA) and 96.01% mean intersection over union (mIoU) on the oilseed rape point cloud segmentation task. The extracted phenotypic parameters were highly correlated with manual measurements, with leaf length and width, leaf area and leaf inclination R2 of 0.9843, 0.9632, 0.9806 and 0.8890, and RMSE of 0.1621 cm, 0.1546 cm, 0.6892 cm2 and 2.1144°, respectively. This technique provides a feasible solution for high-throughput and rapid measurement of seedling phenotypes in oilseed rape.

Plant phenotyping relevance

3D点群再構成・セグメンテーション・特徴抽出法を開発し、油糧ナタネの葉形態形質を手測定と検証しており、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe the authors' oilseed rape multi-view image collection, Gaussian splatting reconstruction, and CKG-PointNet++ segmentation pipeline, but contain no data availability statement, no public dataset deposit, and no author code release with a public URL. The only URLs present are the MDPI DOI and

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