Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:11:54.742293Z
Paper Citation Record · LEDGER
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data
As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.06336.
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 2026-08-09T06:31:02.800959+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
29 of 29 outbound references displayed
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- verified fuzzy23
- unresolved6
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 80fd1e09-2a38-4025-8f97-b06924eaa52b · outbound
This paper cites M., Kim, Y., Kim, J., Kim, S.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data M., Kim, Y., Kim, J., Kim, S
Reference 1
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e67a8a2d-dba5-44a0-9278-a4ff131bd826 · outbound
This paper cites Sq-100x-ss original quantum sensor.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Sq-100x-ss original quantum sensor
Reference 2
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Source-reported events for the cited work
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Observation 0a58ebd0-647d-4fcb-beeb-6831433a8e28 · outbound
This paper cites Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives
Reference 3
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Source-reported events for the cited work
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Observation 32681ba3-8ac7-4037-b8c3-ea1d6f0c67ae · outbound
This paper cites xLSTM: Extended Long Short-Term Memory.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data xLSTM: Extended Long Short-Term Memory
Reference 4
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b7f24fd8-1975-45e9-bb29-5025f9a9b7c4 · outbound
This paper cites Hydroponic lettuce handbook.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Hydroponic lettuce handbook
Reference 5
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Source-reported events for the cited work
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Observation e77ac8ed-a3d0-406f-bbdd-966bb7a93cfa · outbound
This paper cites G., Ku, J., Poli, M., Brockman, G., Chang, D., Gonzalez, G.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data G., Ku, J., Poli, M., Brockman, G., Chang, D., Gonzalez, G
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation df417298-2729-469d-9d0e-5f220a1a1af1 · outbound
This paper cites Deterministic edge-preserving regularization in computed imaging.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Deterministic edge-preserving regularization in computed imaging
Reference 7
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Source-reported events for the cited work
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Observation 2465f059-5aad-4756-9fb5-28bbcc4fb036 · outbound
This paper cites an unresolved cited work.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Unresolved cited work
Reference 8
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 394ca3a0-d217-407b-90bb-12bbf0127e11 · outbound
This paper cites M., Kraus, O., Victors, M., Arumugam, L., Vuggumudi, K., Urbanik, J., Hansen, K., Celik, S., Cernek, N., Jagannathan, G., et al.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data M., Kraus, O., Victors, M., Arumugam, L., Vuggumudi, K., Urbanik, J., Hansen, K., Celik, S., Cernek, N., Jagannathan, G., et al
Reference 9
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Source-reported events for the cited work
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Observation 53108c85-c0cc-4337-ad8d-a17aadcd5421 · outbound
This paper cites L., Boote, K.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data L., Boote, K
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 60472c1b-de54-45a0-9e01-9b47042652eb · outbound
This paper cites and Schmidhuber, J.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data and Schmidhuber, J
Reference 11
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1de65671-ed23-474f-8a07-2ac04520dd05 · outbound
This paper cites Plant growth curves.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Plant growth curves
Reference 12
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3bf8edd8-6735-4dc0-8d4f-cd212762dab9 · outbound
This paper cites Intel® realsense™ depth camera d455.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Intel® realsense™ depth camera d455
Reference 13
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f614bee6-79db-4fad-b188-b2aad264f006 · outbound
This paper cites High-throughput plant phenotyping platform (ht3p) as a novel tool for estimating agronomic traits from the lab to the field.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data High-throughput plant phenotyping platform (ht3p) as a novel tool for estimating agronomic traits from the lab to the field
Reference 14
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0e2d03dc-1764-481d-9ff5-38dddb74cf5b · outbound
This paper cites Self-supervised learning: Generative or contrastive.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Self-supervised learning: Generative or contrastive
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8ef45c4b-1f30-4d30-979d-9438fd8a4cfc · outbound
This paper cites B., Jordan, M.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data B., Jordan, M
Reference 16
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f433fbc2-a693-4b05-8868-02265f83740b · outbound
This paper cites Decoupled Weight Decay Regularization.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Decoupled Weight Decay Regularization
Reference 17
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 69c5dabd-a95b-4a60-ab9b-dcb7d03a60ce · outbound
This paper cites X., Kraus, O.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data X., Kraus, O
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 64f19fe8-096c-4ecf-967a-580effa2a0fb · outbound
This paper cites Grow method and system, August 26 2021.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Grow method and system, August 26 2021
Reference 19
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a3da53cb-b8fa-41ef-a90a-acd95f4cfd9e · outbound
This paper cites Grow space integration for mobile robots, October 31 2024.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Grow space integration for mobile robots, October 31 2024
Reference 20
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 55461cc0-9fc9-4779-b739-28ab4e4d7776 · outbound
This paper cites The Illusion of State in State-Space Models.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data The Illusion of State in State-Space Models
Reference 21
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e95d9d86-5221-4d75-8e76-ea3192d3fd25 · outbound
This paper cites Using growing degree days to predict plant stages.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Using growing degree days to predict plant stages
Reference 22
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation df5c2fe7-ad9e-43a5-ad00-09927cfb2599 · outbound
This paper cites Machine learning for functional protein design.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Machine learning for functional protein design
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 78b4d9cf-08ed-4a18-853c-c7c1d1eddbba · outbound
This paper cites and Meeussen, W.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data and Meeussen, W
Reference 24
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation af987ba9-c8e2-4bcc-8c40-c958c534751e · outbound
This paper cites T., and Uddin, M.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data T., and Uddin, M
Reference 25
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e7e3ecc7-c1b1-4d5d-be1d-5304397b6801 · outbound
This paper cites M., Stanitsas, P., Ranu, N., Ewer, A., Mancuso, J.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data M., Stanitsas, P., Ranu, N., Ewer, A., Mancuso, J
Reference 26
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 59345984-8cac-4910-a353-e16a14f51001 · outbound
This paper cites Scientific discovery in the age of artificial intelligence.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data Scientific discovery in the age of artificial intelligence
Reference 27
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation dbf173ef-b19b-4324-8794-8d196072b345 · outbound
This paper cites S., Varshney, R.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data S., Varshney, R
Reference 28
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 67d44466-fc1c-4e0f-bf33-83db669970ca · outbound
This paper cites write newline.
Self-supervised learning predicts plant growth trajectories from multi-modal industrial greenhouse data write newline
Reference 29
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.