GinJinn2: Object detection and segmentation for ecology and evolution

Abstract

O_LIProper 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, some of which, we believe, can be avoided. C_LIO_LIHere, 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. C_LIO_LIWe 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. C_LIO_LIGinJinn2 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. C_LI

Plant phenotyping relevance

植物画像から種子、気孔、葉形状などを抽出する深層学習ツールを開発・提示しており、植物表現型取得のためのソフトウェアが中心である。

Code and data availability

The paper's GinJinn2 source code and manual are explicitly stated to be freely available on the authors' GitHub repository. The annotated Seeds, Yellow-sticky-traps, Leucanthemum, and stomata annotation datasets are only promised via GfBio 'will be supplied as soon as available', so they are not yet actionable public;