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 pre...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Applications in Plant Sciences |
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| Online Access: | https://doi.org/10.1002/aps3.70000 |
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| author | Hanane Ariouat Youcef Sklab Edi Prifti Jean‐Daniel Zucker Eric Chenin |
| author_facet | Hanane Ariouat Youcef Sklab Edi Prifti Jean‐Daniel Zucker Eric Chenin |
| author_sort | Hanane Ariouat |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f9fe3e01b8ec400eba83efc847df53b3 |
| institution | Kabale University |
| issn | 2168-0450 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applications in Plant Sciences |
| spelling | doaj-art-f9fe3e01b8ec400eba83efc847df53b32025-08-20T03:53:23ZengWileyApplications in Plant Sciences2168-04502025-03-01132n/an/a10.1002/aps3.70000Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentationHanane Ariouat0Youcef Sklab1Edi Prifti2Jean‐Daniel Zucker3Eric Chenin4Institut de Recherche pour le Développement (IRD) Sorbonne Université UMMISCO, F‐93143, Bondy FranceInstitut de Recherche pour le Développement (IRD) Sorbonne Université UMMISCO, F‐93143, Bondy FranceInstitut de Recherche pour le Développement (IRD) Sorbonne Université UMMISCO, F‐93143, Bondy FranceInstitut de Recherche pour le Développement (IRD) Sorbonne Université UMMISCO, F‐93143, Bondy FranceInstitut de Recherche pour le Développement (IRD) Sorbonne Université UMMISCO, F‐93143, Bondy FranceAbstract 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.https://doi.org/10.1002/aps3.70000deep learningherbarium scanssemantic segmentationtrait classification |
| spellingShingle | Hanane Ariouat Youcef Sklab Edi Prifti Jean‐Daniel Zucker Eric Chenin Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation Applications in Plant Sciences deep learning herbarium scans semantic segmentation trait classification |
| title | Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation |
| title_full | Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation |
| title_fullStr | Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation |
| title_full_unstemmed | Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation |
| title_short | Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation |
| title_sort | enhancing plant morphological trait identification in herbarium collections through deep learning based segmentation |
| topic | deep learning herbarium scans semantic segmentation trait classification |
| url | https://doi.org/10.1002/aps3.70000 |
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