Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:14:16.805628Z
Paper Citation Record · LEDGER
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens
As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.16506.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
47 of 47 outbound references displayed
- verified exact1
- verified fuzzy31
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ebbb638f-8970-4061-ac0b-275339702c6d · outbound
This paper cites A deep learning-based approach for detecting plant organs from digitized herbar- ium specimen images.Ecological Informatics, 69:101590, 2022.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens A deep learning-based approach for detecting plant organs from digitized herbar- ium specimen images.Ecological Informatics, 69:101590, 2022
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.387060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b087e013-5d27-4b6e-9b90-2672b4e221b0 · outbound
This paper cites Extracting masks from herbarium specimen images based on object detection and image segmentation techniques.Biodiversity Information Science and Standards, 7, 2023.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Extracting masks from herbarium specimen images based on object detection and image segmentation techniques.Biodiversity Information Science and Standards, 7, 2023
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.375950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 74524b13-c867-4bac-bbef-b05ab9fd7466 · outbound
This paper cites Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation.Applications in Plant Sciences, 13(2):e70000, 2025.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Enhancing plant morphological trait identification in herbarium collections through deep learning–based segmentation.Applications in Plant Sciences, 13(2):e70000, 2025
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.364531Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 09c2a0ec-3819-499b-a8da-846fc081a441 · outbound
This paper cites Sukhorukov, Alain Vanderpoorten, and Farid Jabbour.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Sukhorukov, Alain Vanderpoorten, and Farid Jabbour
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.349957Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2267557c-97d7-454b-8c32-2cbdaa199e69 · outbound
This paper cites A deep learning based approach for automated plant disease classification using vision transformer.Scientific Reports, 12, 2022.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens A deep learning based approach for automated plant disease classification using vision transformer.Scientific Reports, 12, 2022
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.337372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dbc9d6cb-1ada-4086-8170-8aac25639dc2 · outbound
This paper cites Digitised herbarium image segmenta- tion dataset.https://doi.org/10.6084/m9.figshare.29538065.v2, 2025.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Digitised herbarium image segmenta- tion dataset.https://doi.org/10.6084/m9.figshare.29538065.v2, 2025
Reference 6
- Resolution
- verified exact
- doi, observed 2026-08-06T15:14:16.837991Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation eaf2c8c7-5985-48a2-add5-1c22f208cd22 · outbound
This paper cites Plant region detection in digitised herbarium specimens.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Plant region detection in digitised herbarium specimens
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.304399Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b72f1b8e-f3e4-4a62-904a-7e996bdf5c91 · outbound
This paper cites Robustsam: Segment anything robustly on degraded images.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4081–4091, 2024.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Robustsam: Segment anything robustly on degraded images.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4081–4091, 2024
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.291156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 37b16585-b1e8-48ed-a15d-60417d16202f · outbound
This paper cites A survey of deep convolutional neural networks applied for prediction of plant leaf diseases.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens A survey of deep convolutional neural networks applied for prediction of plant leaf diseases
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.278979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 25d3084e-589b-44cc-9c39-86632b1d0daa · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.663428Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d353cf15-bcf1-441d-9d50-29b2f5dd1327 · outbound
This paper cites A segmentation-guided deep learning framework for leaf counting.Frontiers in Plant Science, 13, 2022.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens A segmentation-guided deep learning framework for leaf counting.Frontiers in Plant Science, 13, 2022
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.259021Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6ed7a446-3787-4a3d-bb8f-f91b6a20ac3e · outbound
This paper cites Doctoral thesis, University of Turin and Muséum National d’Histoire Naturelle of Paris, 2024.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Doctoral thesis, University of Turin and Muséum National d’Histoire Naturelle of Paris, 2024
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.246952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 93c4a889-841e-46a9-8c85-ef716ca15a47 · outbound
This paper cites Deep residual learning for image recognition.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Deep residual learning for image recognition
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.672733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5c818f25-540a-49ce-870c-1325b0340936 · outbound
This paper cites Semantic segmentation of herbarium specimens using deep learning techniques.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Semantic segmentation of herbarium specimens using deep learning techniques
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.228635Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 54a2cd27-3a92-475d-b93b-34a7abb64bf5 · outbound
This paper cites Convolutional neural networks for image-based high-throughput plant phenotyp- ing: a review.Plant Phenomics, 2020:1–15, 2020.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Convolutional neural networks for image-based high-throughput plant phenotyp- ing: a review.Plant Phenomics, 2020:1–15, 2020
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.216559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cd7e4ff0-8787-4c3c-a0c5-f8a98d2d63cd · outbound
This paper cites Segment Anything.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Segment Anything
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.682675Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f992fc82-6a7a-4b3f-b202-e2767c2940a2 · outbound
This paper cites Imagenet classification with deep convolutional neural networks.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Imagenet classification with deep convolutional neural networks
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.686222Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 126068e6-a4cf-4691-b1e9-3e70001f5f40 · outbound
This paper cites Lecun, L.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Lecun, L
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.689878Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7e8fd96-da9c-4717-8ddb-6022b96ab4d1 · outbound
This paper cites an unresolved cited work.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T15:14:17.180724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 31c83824-d2aa-43f7-a2dd-25bceb3321ca · outbound
This paper cites Roles of natural history collections.Annals of the Missouri Botanical Garden, 83(4):536–545, 1996.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Roles of natural history collections.Annals of the Missouri Botanical Garden, 83(4):536–545, 1996
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.159456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 43495384-fb9c-4a1f-bbd5-9c07eaf35891 · outbound
This paper cites Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.147677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e9f037d6-fb33-4a2f-b748-04b2f0aa3fab · outbound
This paper cites Ott and U.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Ott and U
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.136676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6b3cc880-4be1-40d6-b4c3-4abc52c88d5b · outbound
This paper cites an unresolved cited work.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T15:14:17.125791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a212e868-7c23-4d96-9edb-80a94d5b2ce1 · outbound
This paper cites an unresolved cited work.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Unresolved cited work
Reference 24
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T15:14:17.114930Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3950429d-b630-42ff-9a7a-1b7acf11b9e4 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens SAM 2: Segment Anything in Images and Videos
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.714519Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fddcf902-bda2-4ad6-a9c7-56f097d56fea · outbound
This paper cites Leveraging multimodal- ity for biodiversity data: Exploring joint representations of species descriptions and specimen images using CLIP.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Leveraging multimodal- ity for biodiversity data: Exploring joint representations of species descriptions and specimen images using CLIP
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.104245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a35fb8be-38c2-4d84-931c-51881e18a8bd · outbound
This paper cites Very deep convolutional networks for large-scale image recognition.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Very deep convolutional networks for large-scale image recognition
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.094114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 54d1fe84-c2d6-4b99-9d03-791ddf220bb8 · outbound
This paper cites Towards a deep learning-powered herbarium image analysis platform.Biodiversity Information Science and Standards, 2024.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Towards a deep learning-powered herbarium image analysis platform.Biodiversity Information Science and Standards, 2024
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.084604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fc1440b6-e004-469d-9de1-94b95ef0adcb · outbound
This paper cites Sim-net: A multimodal fusion network using inferred 3d object shape point clouds from rgb images for 2d classification.IET Computer Vision, 19(1):e70036, 2025.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Sim-net: A multimodal fusion network using inferred 3d object shape point clouds from rgb images for 2d classification.IET Computer Vision, 19(1):e70036, 2025
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.074649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5a82acde-927e-43b7-8814-9e3e78c89c0d · outbound
This paper cites Identification of non-plant elements in herbarium images using yolo.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Identification of non-plant elements in herbarium images using yolo
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.064471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9d5cfe78-9f8d-4a5d-9cfd-ffcd7b7e0180 · outbound
This paper cites Herbarium image segmentation dataset with plant masks for enhanced morphological trait analysis.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Herbarium image segmentation dataset with plant masks for enhanced morphological trait analysis
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.050965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1b8bc302-1105-411a-a965-648f9c37bade · outbound
This paper cites Digitization of herbaria enables novel research.American Journal of Botany, 104:1281–1284, 2017.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Digitization of herbaria enables novel research.American Journal of Botany, 104:1281–1284, 2017
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.038426Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 85c6b55c-4ee5-410e-89fd-efeb1daa9bca · outbound
This paper cites Sweeney, Binil Sajan, Paul J.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Sweeney, Binil Sajan, Paul J
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.027100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d237ce5a-0d90-42a4-b472-fe0b3a6ad26a · outbound
This paper cites Szegedy, Wei Liu, Yangqing Jia, P.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Szegedy, Wei Liu, Yangqing Jia, P
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:17.013176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e30e20a0-481a-4c05-a452-c428be235fed · outbound
This paper cites an unresolved cited work.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Unresolved cited work
Reference 35
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T15:14:17.002266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 62afa03d-659c-4205-a21c-db35c08a1f03 · outbound
This paper cites Deep leaf: Mask r-cnn based leaf detection and segmentation from digitized herbarium specimen images.Pattern Recognition Letters, 150:76–83, 2021.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Deep leaf: Mask r-cnn based leaf detection and segmentation from digitized herbarium specimen images.Pattern Recognition Letters, 150:76–83, 2021
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:16.991499Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 670774cf-71d0-4f0d-9c17-3ffefc4c9603 · outbound
This paper cites Deep learning based approach for digitized herbarium specimen segmentation.Multimedia Tools and Applications, 81, 08 2022.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Deep learning based approach for digitized herbarium specimen segmentation.Multimedia Tools and Applications, 81, 08 2022
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:16.981254Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 496ca838-91e9-4e0a-a7aa-2cf0c38d6b54 · outbound
This paper cites YOLOv10: Real-Time End-to-End Object Detection.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens YOLOv10: Real-Time End-to-End Object Detection
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.761169Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8a67b3b4-67bd-4a7a-a758-6fb9f2ea68f7 · outbound
This paper cites Weaver and Stephen A.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Weaver and Stephen A
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:16.966496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6d5776a0-e096-4939-be11-3381d1cc0952 · outbound
This paper cites an unresolved cited work.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Unresolved cited work
Reference 40
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T15:14:16.954697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 95ac1d3d-8e49-4a60-ba66-9d68e31257f5 · outbound
This paper cites Wilde, Jason G.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Wilde, Jason G
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:16.943824Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4d3c4eca-1e90-417a-a3b2-2cced1d7e7ab · outbound
This paper cites Sams : One-shot learning for the segment anything model using similar images.2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2024.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Sams : One-shot learning for the segment anything model using similar images.2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2024
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:16.933737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6b19ac29-9138-43bb-83f9-0d0c640f13d8 · outbound
This paper cites De- tection and annotation of plant organs from digitised herbarium scans using deep learning.Biodiversity Data Journal, 8:1–10, 2020.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens De- tection and annotation of plant organs from digitised herbarium scans using deep learning.Biodiversity Data Journal, 8:1–10, 2020
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:16.923029Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a5d555d2-3ad5-4c5b-9acd-b550a280ad71 · outbound
This paper cites Personalize Segment Anything Model with One Shot.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Personalize Segment Anything Model with One Shot
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.786887Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ad092ee6-965a-450d-8672-6c9b89293573 · outbound
This paper cites Deep-learning-based in-field citrus fruit detection and tracking.Horticulture Research, 9, 2022.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Deep-learning-based in-field citrus fruit detection and tracking.Horticulture Research, 9, 2022
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:14:16.912153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 040cc4e7-0b1e-4a73-87b8-1174602f0b76 · outbound
This paper cites Fast Segment Anything.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens Fast Segment Anything
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.802099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 64d34148-d015-4c41-8907-5da1b4a42bcf · outbound
This paper cites UNet++: A Nested U-Net Architecture for Medical Image Segmentation.
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:14:16.805628Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.