Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmentation.

Abstract

Premise Deep learning has become increasingly important in the analysis of digitized herbarium collections, which comprise millions of scans that provide valuable resources for studying plant evolution and biodiversity. However, leveraging deep learning algorithms to analyze these scans presents significant challenges, partly due to the heterogeneous nature of the non-plant material that forms the background of the scans. We hypothesize that removing such backgrounds can improve the performance of these algorithms. Methods We propose a novel method based on deep learning to segment and generate plant masks from herbarium scans and subsequently remove the non-plant backgrounds. The semi-automatic preprocessing stages involve the identification and removal of non-plant elements, substantially reducing the manual effort required to prepare the training dataset. Results The results highlight the importance of effective image segmentation, which achieved an F1 score of up to 96.6%. Moreover, when used in classification models for plant morphological trait identification, the images resulting from segmentation improved classification accuracy by up to 3% and F1 score by up to 7% compared to non-segmented images. Discussion Our approach isolates plant elements in herbarium scans by removing background elements to improve classification tasks. We demonstrate that image segmentation significantly enhances the performance of plant morphological trait identification models.

Plant phenotyping relevance

ハーバリウム画像から植物マスクを生成する深層学習セグメンテーション手法を開発し、形態形質識別への効果も検証しており、表現型取得・抽出手法が中心である。

Code and data availability

The paper's Data Availability Statement explicitly deposits the authors' segmentation source code and trained models on GitHub and the herbarium image–mask dataset (2277 image–mask pairs) on figshare; both are paper-specific, public, and actionable.