MEP3D: Improved clustering-based 3D point cloud method for comprehensive maize ear phenotypic trait extraction

Abstract

Maize ear traits are critical indicators for elucidating yield formation mechanisms and are widely used in genetic studies. Traditional two-dimensional (2D) phenotyping suffers from planar analysis constraints, occlusions in single-view imaging, and limited robustness to mixed textures or curved ears. To address these issues and support germplasm archiving and breeding research, we developed MaizeEar3DPheno (MEP3D), a 3D point cloud-based method for quantifying maize ear phenotypic traits. A structured-light 3D scanning system equipped with a motorized rotary platform was designed to acquire point clouds of 30 maize ears from three different varieties. Preprocessing involved axis alignment via PCA, uniform downsampling, and removal of non-kernel regions. Following preprocessing, key phenotypic traits, including ear length, diameter, and barren tip length, were calculated from the processed point clouds. MEP3D integrated directional erosion with density-based clustering to achieve robust kernel segmentation and counting. A spatial analysis algorithm was further developed to locate kernel row arrangements from geometric features. The results demonstrated that the proposed method achieved high-precision cross-variety kernel counting, with a mean absolute percentage error (MAPE) of 0.91%, and a coefficient of determination (R²) of 0.9917 across all maize ears. Kernel row quantification was fully consistent with manual measurements, allowing extraction of row inclination and average kernel number per row. Ear length, diameter, and barren tip length estimation achieved R² values of 0.9864, 0.9871, and 0.9670, respectively, demonstrating robustness. The generated high-fidelity 3D phenotypic data supports automated evaluation of ear and kernel traits and facilitates in-depth analysis of spatial morphological characteristics.

Plant phenotyping relevance

トウモロコシ雌穂の3D点群取得・処理・形質抽出法を開発し、カーネル数や穂長などを手動測定と比較検証しており、フェノタイピング手法が研究の中心である。

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