Fruit Morphological Measurement Based on Three-Dimensional Reconstruction

Abstract

Three-dimensional (3D) shape information is valuable for fruit quality evaluation. Grading of the fruits is one of the important postharvest tasks that the fruit processing agro-industries do. Although the internal quality of the fruit is important, the external quality of the fruit influences the consumers and the market price significantly. To solve the problem of feature size extraction in 3D fruit scanning, this paper proposes an automatic fruit measurement scheme based on a 2.5-dimensional point cloud with a Kinect depth camera. For getting a complete fruit model, not only the surface point cloud is obtained, but also the bottom point cloud is rotated to the same coordinate system, and the whole fruit model is obtained by iterative closest point algorithm. According to the centroid and principal direction of the fruit, the cut plane of the fruit is made in the x-axis, y-axis, and z-axis respectively to obtain the contour line of the fruit. The experiment is divided into two groups, the first group is various sizes of pears to get the morphological parameters; the second group is the various colors, shapes, and textures of many fruits to get the morphological parameters. Comparing the predicted value with the actual value shows that the automatic extraction scheme of the size information is effective and the methods are universal and provide a reference for the development of the related application.

Plant phenotyping relevance

Kinect深度カメラと3D再構成を用いて果実形態・サイズ形質を自動抽出する手法を開発し、実測値との比較で検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper describes Kinect-based 3D fruit point cloud reconstruction and morphological measurements, but contains no data availability statement, no public phenotype dataset, no deposited point clouds or images, and no author code repository. The only URL-like reference (MathWorks community profile) is a cited spline-f

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