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Paper Citation Record · LEDGER

Graph-Based Deep Learning for Component Segmentation of Maize Plants

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.00182.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.00182 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:25:52.633659Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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  • verified fuzzy6
  • unresolved21
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External citation measurements

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Outbound references

Observation d0a8bf8f-e3f1-442a-b864-59920e542e99 · outbound

This paper cites Carroll, C.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Carroll, C

Reference 1

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doi, observed 2026-08-06T21:25:53.220031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 329d4b9d-15b8-4ccc-9f15-09e09c1deb92 · outbound

This paper cites Zhang, et al., Biomass and crop height estimation of different crops using uav-based 3d point cloud, Remote Sensing 12 (1) (2020) 17.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Zhang, et al., Biomass and crop height estimation of different crops using uav-based 3d point cloud, Remote Sensing 12 (1) (2020) 17

Reference 2

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raw_fallback, observed 2026-08-06T21:25:56.507138Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 0c043d52-f3e2-41a8-8ccd-b0d28164a3b1 · outbound

This paper cites URL https://geopard.tech/blog/crop-identification/.

Graph-Based Deep Learning for Component Segmentation of Maize Plants URL https://geopard.tech/blog/crop-identification/

Reference 3

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e0f2efbd-941b-4762-ba71-7d5103695b44 · outbound

This paper cites URL https://www.picsellia.com/post/ precision-agriculture-computer-vision-crop-health-monitoring.

Graph-Based Deep Learning for Component Segmentation of Maize Plants URL https://www.picsellia.com/post/ precision-agriculture-computer-vision-crop-health-monitoring

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 65a0ba83-1714-478d-8e58-b5ff4174627d · outbound

This paper cites Mahlein, Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping, Plant Disease 100 (2) (2016) 241–251.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Mahlein, Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping, Plant Disease 100 (2) (2016) 241–251

Reference 5

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raw_fallback, observed 2026-08-06T21:25:55.967462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 3fae0de0-2fec-450c-8a16-a9e22aa2de2c · outbound

This paper cites URL https://geopard.tech/blog/predicting-crop-yield-with-remote-sensing-data/.

Graph-Based Deep Learning for Component Segmentation of Maize Plants URL https://geopard.tech/blog/predicting-crop-yield-with-remote-sensing-data/

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 496a014c-41aa-4eeb-b25a-4d7b65192626 · outbound

This paper cites Bertolini, D.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Bertolini, D

Reference 7

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Observation 1eed2237-fef7-4eff-9dd9-3372e5e352bc · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 13d2cfc5-20c3-44f3-a283-4196dfff7112 · outbound

This paper cites An Introduction to Convolutional Neural Networks.

Graph-Based Deep Learning for Component Segmentation of Maize Plants An Introduction to Convolutional Neural Networks

Reference 9

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Observation 9fbe66b3-f321-44a8-8465-6381425ab5e0 · outbound

This paper cites Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping

Reference 10

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local_arxiv, observed 2026-08-06T21:25:53.682178Z

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Observation d5dc29ef-76a9-4454-9916-d0d4ab33d2eb · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 11

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Observation 2d3184b5-0323-49a4-b1eb-db771ed13218 · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

Graph-Based Deep Learning for Component Segmentation of Maize Plants PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 12

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Observation 63d3b63d-853d-4941-98fe-038c37f651a7 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 14

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Observation 4e1f4694-dd79-49ef-8284-1161587b540e · outbound

This paper cites Dynamic Graph CNN for Learning on Point Clouds.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Dynamic Graph CNN for Learning on Point Clouds

Reference 15

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Observation 34590028-bb2d-4d39-be9d-6b0951012516 · outbound

This paper cites Graph Attention Networks.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Graph Attention Networks

Reference 16

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Observation 012a534a-2018-4eb1-9f23-bb374a488780 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 17

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 6c121a0c-5b4e-4a43-a645-14dbd529ea40 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Semi-Supervised Classification with Graph Convolutional Networks

Reference 18

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Observation abe3af2d-9b84-486a-b4c5-019575834467 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d9b731de-0552-4375-9cf3-ba1b5a65a630 · outbound

This paper cites Hotelling, Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24 (6) (1933) 417–441.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Hotelling, Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24 (6) (1933) 417–441

Reference 20

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Observation d3833e5a-dc56-4401-91c3-13fa08c33d36 · outbound

This paper cites Pearson, On lines and planes of closest fit to systems of points in space, Philosophical Magazine 2 (11) (1901) 559–572.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Pearson, On lines and planes of closest fit to systems of points in space, Philosophical Magazine 2 (11) (1901) 559–572

Reference 21

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 48e3e515-cb43-45da-abc7-359567dc1ab3 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 22

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Observation ab7703d5-a69c-4256-bc3c-74d1700da1c9 · outbound

This paper cites Weinmann, B.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Weinmann, B

Reference 23

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b5586fbb-73ca-4feb-8eaf-91a5bafa18bc · outbound

This paper cites Full Duplex Hybrid A/D Beamforming with Reduced Complexity Multi-Tap Analog Cancellation.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Full Duplex Hybrid A/D Beamforming with Reduced Complexity Multi-Tap Analog Cancellation

Reference 24

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Observation 833c33b0-ff91-4bac-a668-b97aedbac0ef · outbound

This paper cites Hackel, J.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Hackel, J

Reference 25

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 31652d88-69ce-4ffa-89a9-3cd720836554 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 26

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Observation 7bcb8b59-9dd0-4dc5-9331-5b7c43fc0a1c · outbound

This paper cites PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.

Graph-Based Deep Learning for Component Segmentation of Maize Plants PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Reference 27

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Observation 976b4c7d-7555-44b1-8fc1-6d15d7625240 · outbound

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Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 28

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 34f5209e-afa6-45f6-85b4-afa3e027106d · outbound

This paper cites DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.

Graph-Based Deep Learning for Component Segmentation of Maize Plants DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Reference 29

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Observation 3d984c2c-add2-42d5-a38c-8cf3240d5dc2 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 30

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Observation 709b67a1-b440-4e7f-b7b2-17337238a707 · outbound

This paper cites MLP-Mixer: An all-MLP Architecture for Vision.

Graph-Based Deep Learning for Component Segmentation of Maize Plants MLP-Mixer: An all-MLP Architecture for Vision

Reference 31

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Observation 850d070a-38a0-4c53-8d73-9f0d645d0081 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 32

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Observation e2adda95-9118-4b91-800b-bce575e0d8a5 · outbound

This paper cites Vaswani, N.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Vaswani, N

Reference 33

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Observation 16b5e3e7-18dd-44f4-990d-ec1842242270 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Adam: A Method for Stochastic Optimization

Reference 34

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Observation 2314d4d8-dbcf-41f4-a40e-edba916870c3 · outbound

This paper cites an unresolved cited work.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Unresolved cited work

Reference 35

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation dce4b653-159c-4137-a3e1-cd8b2a9a703e · outbound

This paper cites Schunck, F.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Schunck, F

Reference 36

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Observation f80315dc-c722-4283-9bfc-8df74161fc38 · outbound

This paper cites Cignoni, M.

Graph-Based Deep Learning for Component Segmentation of Maize Plants Cignoni, M

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers

No inbound Pith citation observations are available.