Abstract
Recognizing and classifying multiple morphological features, such as patterns, sizes, and textures, can provide a comprehensive understanding of their variability and phenotypic evolution. Yet, quantitatively measuring complex morphological characters remains challenging.We provide a machine learning-based pipeline (SVMorph) to consider and classify complex morphological characters in multiple organisms that have either small or large datasets.Our pipeline integrates two descriptors, histogram of oriented gradient and local binary pattern, to meet various classification needs. We also optimized feature extraction by adding image data augmentation to improve model generalizability.We tested SVMorph on two real-world examples to demonstrate that it can be used with small training datasets and limited computational resources. Comparing with multiple CNN-based methods and traditional techniques, we show that SVMorph is reliable and fast in texture-based individual classification. Thus, SVMorph can be used to efficiently classify multiple morphological characters in distinct nonmodel organisms.