Premise: The generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor‐intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens.Methods and Results: We implemented an extendable pipeline based on state‐of‐the‐art deep‐learning object‐detection methods to collect leaf images from herbarium specimens of two species of the genus Leucanthemum. Using 183 specimens as the training data set, our pipeline extracted one or more intact leaves in 95% of the 61 test images.Conclusions: We establish GinJinn as a deep‐learning object‐detection tool for the automatic recognition and extraction of individual leaves or other structures from herbarium specimens. Our pipeline offers greater flexibility and a lower entrance barrier than previous image‐processing approaches based on hand‐crafted features.

The description of biological objects, such as seeds, mainly relies on manual measurements of few characteristics, and on visual classification of structures, both of which can be subjective, error prone and time‐consuming. Image analysis tools offer means to address these shortcomings, but we currently lack a method capable of automatically handling seeds from different taxa with varying morphological attributes and obtaining interpretable results. Here, we provide a simple image acquisition and processing protocol and introduce Traitor, an open‐source software available as a command‐line interface (CLI), which automates the extraction of seed morphological traits from images. The workflow for trait extraction consists of scanning seeds against a high‐contrast background, correcting image colours, and analysing images with the software. Traitor is capable of processing hundreds of images of varied taxa simultaneously with just three commands, and without a need for training, manual fine‐tuning or thresholding. The software automatically detects each object in the image and extracts size measurements, traditional morphometric descriptors widely used by scientists and practitioners, standardised shape coordinates, and colorimetric measurements. The method was tested on a dataset comprising of 91,667 images of seeds from 1228 taxa. Traitor's extracted average length and width values closely matched the average manual measurements obtained from the same collection (concordance correlation coefficient of 0.98). Further, we used a large image dataset to demonstrate how Traitor's output can be used to obtain representative seed colours for taxa, determine the phylogenetic signal of seed colour, and build objective classification categories for shape with high levels of visual interpretability. Our approach increases productivity and allows for large‐scale analyses that would otherwise be unfeasible. Traitor enables the acquisition of data that are readily comparable across different taxa, opening new avenues to explore functional relevance of morphological traits and to advance on new tools for seed identification.

Collection and preparation of empirical data still represent one of the most important, but also expensive steps in ecological and evolutionary/systematic research. Modern machine learning approaches, however, have the potential to automate a variety of tasks, which until recently could only be performed manually. Unfortunately, the application of such methods by researchers outside the field is hampered by technical difficulties. Here, we present GinJinn2, a user‐friendly toolbox for deep learning‐based object detection and instance segmentation on image data. Besides providing a convenient command‐line interface to existing software libraries, it comprises several additional tools for data handling, pre‐ and postprocessing, and building advanced analysis pipelines. We demonstrate the application of GinJinn2 for biological purposes using four exemplary analyses, namely the evaluation of seed mixtures, detection of insects on glue traps, segmentation of stomata and extraction of leaf silhouettes from herbarium specimens. GinJinn2, by providing a coding‐free environment, will enable users with a primary background in biology to apply deep learning‐based methods for object detection and segmentation in order to automate feature extraction from image data.

Premise: Most phylogenomic library preparation methods and bioinformatic analysis tools in restriction site–associated DNA sequencing (RADseq)/genotyping‐by‐sequencing (GBS) studies are designed for use with Illumina data. The lack of alternative bioinformatic pipelines hinders the exploration of long‐read multi‐locus data from other sequencing platforms. The Simple Long‐read loci Assembly of Nanopore data for Genotyping (SLANG) pipeline enables locus assembly, orthology estimation, and single‐nucleotide polymorphism (SNP) calling using Nanopore‐sequenced multi‐locus data.Methods and Results: Two test libraries (Leucanthemum spp., Senecio spp.; Compositae) were prepared using an amplified fragment length polymorphism (AFLP)‐based method to reduce genome complexity, then Nanopore‐sequenced, and analyzed with SLANG. We identified 704 and 448 orthologous loci with 12,368 and 10,048 SNPs, respectively. The constructed phylogenetic networks were identical to a GBS network produced using Leucanthemum Illumina data and were consistent with Senecio species circumscriptions based on morphology.Conclusions: SLANG identifies orthologous loci and extracts SNPs from long‐read multi‐locus Nanopore data for phylogenetic inference, population genetics, or phylogeographical studies. Combined with an AFLP‐based library preparation, SLANG provides an easily scalable, cost‐effective, and affordable alternative to Illumina‐based RADseq/GBS procedures.