Paper Citation Record · LEDGER

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management

As of 23 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2507.08024.

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

pith.paper-citation-record.v1
2507.08024 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:11:23.845273Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:11:23.756991Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:11:23.915046Z

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 908984e7-08ef-45f6-9766-9ff2360aa39d · outbound

This paper cites Central to this trans- formation is the automated analysis of agricultural images for real-time crop monitoring, disease detection, and treatment recommendation.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Central to this trans- formation is the automated analysis of agricultural images for real-time crop monitoring, disease detection, and treatment recommendation

Reference 1

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Observation e1396204-1064-4159-8ebf-6c0b4ac16d81 · outbound

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 2

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Observation 5917bbe5-fa79-41c9-8425-421e7ae3a689 · outbound

This paper cites Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management

Reference 3

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 4

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This paper cites maize leaf blight, severe infection symp- toms: tan to grayish spots with darker borders, analysis: se- vere tan spots require mancozeb.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management maize leaf blight, severe infection symp- toms: tan to grayish spots with darker borders, analysis: se- vere tan spots require mancozeb

Reference 5

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 6

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 7

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Observation cb01a9a8-d8ac-463b-b87a-be4f89614d96 · outbound

This paper cites spots” vs. “le- sions.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management spots” vs. “le- sions

Reference 8

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 9

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Observation 2ec9948e-5166-497b-bfb5-498def1ee0b8 · outbound

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 10

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Observation fe0a02e1-540f-45f1-9a1f-285db902c539 · outbound

This paper cites Domain-Aware Embedding: Encode each response using the agricultural domain-adapted embed- ding model 3.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Domain-Aware Embedding: Encode each response using the agricultural domain-adapted embed- ding model 3

Reference 11

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Observation cbc49f98-a919-4c2f-9f58-c4960210ea99 · outbound

This paper cites Winners %.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Winners %

Reference 12

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 13

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Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Unresolved cited work

Reference 14

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Observation f709c2f9-73b2-4c7e-94db-95b3f2475898 · outbound

This paper cites Precision agricul- ture: A worldwide overview,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Precision agricul- ture: A worldwide overview,

Reference 15

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Observation 6c7ded8a-c83e-4599-87cc-e26cf6804816 · outbound

This paper cites Under- standing small-scale farmers’ perception and adaption strategies to climate change impacts: Evidence from two agro-ecological zones bordering national parks of uganda,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Under- standing small-scale farmers’ perception and adaption strategies to climate change impacts: Evidence from two agro-ecological zones bordering national parks of uganda,

Reference 16

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Observation 016daf2c-8879-4548-8756-8b3cedbae622 · outbound

This paper cites Pairwise feature learning for unseen plant disease recognition,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Pairwise feature learning for unseen plant disease recognition,

Reference 17

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Observation cfb20164-16a4-4c94-8370-fe308a8467ef · outbound

This paper cites Using deep learning for image-based plant disease detection,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Using deep learning for image-based plant disease detection,

Reference 18

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Observation e1df20cb-9ce4-4a9b-81f7-41e67e8074ed · outbound

This paper cites Learning transferable visual models from nat- ural language supervision,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Learning transferable visual models from nat- ural language supervision,

Reference 19

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Observation 1a0c8e5c-60da-425d-971d-884fee1975d3 · outbound

This paper cites Visual large language model for wheat disease diagno- sis in the wild,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Visual large language model for wheat disease diagno- sis in the wild,

Reference 20

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Observation d755138a-e4a5-422f-8a16-9586bb57aaf9 · outbound

This paper cites A framework for agricultural intelli- gent analysis based on a visual language large model,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management A framework for agricultural intelli- gent analysis based on a visual language large model,

Reference 21

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Observation efbe6ff3-ab47-49ef-aa82-43f55c2177e9 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 22

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Observation 4978ed1e-7dee-43d0-b88e-39bd0cc18ed6 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Chain-of-thought prompting elicits reasoning in large language models,

Reference 23

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Observation 6699e3a8-fbce-4527-96d1-d4527370fd63 · outbound

This paper cites Universal Self-Consistency for Large Language Model Generation.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Universal Self-Consistency for Large Language Model Generation

Reference 24

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This paper cites AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning

Reference 25

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This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Sentence-bert: Sentence embeddings using siamese bert-networks,

Reference 26

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Observation a6520699-aec8-4be5-b414-c15e2ba1af70 · outbound

This paper cites Bleurt: Learning robust metrics for text generation,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Bleurt: Learning robust metrics for text generation,

Reference 27

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This paper cites Evaluating deep learning techniques for natural lan- guage inference,.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Evaluating deep learning techniques for natural lan- guage inference,

Reference 28

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Observation 171e1b20-ce44-4e00-b1cd-5ac66af30091 · outbound

This paper cites Introducing openai o1 pre- view.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management Introducing openai o1 pre- view

Reference 29

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Observation 3013bdf6-651d-427c-8cdb-fe7fa97c72f9 · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management PaliGemma: A versatile 3B VLM for transfer

Reference 30

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Observation 51712cf1-90a0-4f38-a168-b181d8b8db9b · outbound

This paper cites all-MiniLM-L6-v2: A lightweight sentence transformer model.

Self-Consistency in Vision-Language Models for Precision Agriculture: Multi-Response Consensus for Crop Disease Management all-MiniLM-L6-v2: A lightweight sentence transformer model

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:11:23.930959Z

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Pith citing papers

Observation 5917bbe5-fa79-41c9-8425-421e7ae3a689 · inbound