Three-dimensional image analysis for almond endocarp feature extraction and shape description

We propose a morphological characterization of the endocarp of the fruit of the almond tree, Prunus amygdalus (Batsch), using computer vision techniques to extract features in 3D almond endocarp meshes with the objective to describe the diversity of the crop in a systematic and unambiguous form. All the proposed descriptors are quantitative and easily computable, allowing fast and objective assessments of the morphological variations between almond varieties. We collect and 3D-scan a total of 9510 almond endocarps to obtain such meshes, to which we apply an affine transformation so that they are positioned in a standardized reference where meaningful physical measures can be taken. Complex descriptors derived from the geometry of the endocarp are then introduced to identify richer features. The use of 3D, compared to simply taking 2D images, allows for a more accurate and complete description of the endocarp shape. In particular, the contour and apex shapes, keel development, markings on the surface, and symmetry of the endocarp are analyzed and given quantitative measures. The validity of the presented morphological descriptors is finally tested on 2610 endocarps from the collected dataset, corresponding to 36 autochthonous almond varieties from the island of Mallorca (Spain) and 14 international reference varieties, all with well documented characteristics. Numerical results show that the proposed descriptors agree with human-made shape classifications of the studied varieties with a coincidence of $\text{75.0}\text{\%}$ for contour shape, $\text{76.0}\text{\%}$ for apex shape, and $\text{80.0}\text{\%}$ for keel development. Visual comparisons of the extracted features also show that they are coherent with commonly used guidelines for the morphological characterization of the almond endocarp. We conclude that the use of 3D imaging approaches for the description of the almond endocarp is a promising alternative to traditional methods, providing a reliable way to deal with ambiguity and helping reduce biases and inconsistencies caused by subjective visual evaluations.