Paper Citation Record · LEDGER

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2506.22570.

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

pith.paper-citation-record.v1
2506.22570 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:08:27.816223Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

33 of 33 outbound references displayed

verified exact2
verified fuzzy26
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cb4c878-0aac-4e43-9eac-8215c2dd984c · outbound

This paper cites Self-Supervised Learning for Image Segmentation: A Comprehensive Survey.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Self-Supervised Learning for Image Segmentation: A Comprehensive Survey

Reference 1

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Observation 6fca1a2f-5c9e-4559-9be7-71617bdcc632 · outbound

This paper cites Ai- based uav swarms for monitoring and disease identification of brassica plants using machine learning: A review.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Ai- based uav swarms for monitoring and disease identification of brassica plants using machine learning: A review

Reference 2

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Observation 0616b2a6-9f24-47bd-a159-6943004fb786 · outbound

This paper cites Reinforcement learning for sustainable agriculture.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Reinforcement learning for sustainable agriculture

Reference 3

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Observation ed51954d-bf1b-4a4d-8085-b7dc4f587b55 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 4

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This paper cites Agriculture-vision: A large aerial image database for agricultural pattern analysis.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Agriculture-vision: A large aerial image database for agricultural pattern analysis

Reference 5

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Observation 8601d69d-a6f8-4306-b3c3-fa64bdc59bd4 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 6

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Observation 10256063-4356-4997-9634-1681dafcdc87 · outbound

This paper cites An accurate semantic segmentation model for bean seedlings and weeds identification based on improved erfnet.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation An accurate semantic segmentation model for bean seedlings and weeds identification based on improved erfnet

Reference 7

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Observation 1996041c-3919-43fe-8893-0013d9aa8c7e · outbound

This paper cites Computer vision in smart agriculture and precision farm- ing: Techniques and applications.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Computer vision in smart agriculture and precision farm- ing: Techniques and applications

Reference 8

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Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Unresolved cited work

Reference 9

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Observation 4b034bee-c62d-4c5b-80b5-f7f6c6253abf · outbound

This paper cites Global context vision trans- formers.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Global context vision trans- formers

Reference 10

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Observation b34e6d8a-6225-4261-acc2-7504f0f2ab13 · outbound

This paper cites Le, and Hartwig Adam.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Le, and Hartwig Adam

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:32.962795Z

Source-reported events for the cited work

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Observation 4b6251dc-0aac-4217-ad20-0bffb57e7ba2 · outbound

This paper cites Improved semi-supervised attention gan for seman- tic segmentation.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Improved semi-supervised attention gan for seman- tic segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:32.784153Z

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Observation e56e97a4-cc22-40c9-9e10-15fe793465e8 · outbound

This paper cites Geminifusion: Effi- cient pixel-wise multimodal fusion for vision transformer.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Geminifusion: Effi- cient pixel-wise multimodal fusion for vision transformer

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:32.536467Z

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Observation 1a77711c-0a26-4ab1-830f-ca9e0ddc1a80 · outbound

This paper cites Segmentation of farmlands in aerial images by deep learning framework with feature fusion and context aggregation modules.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Segmentation of farmlands in aerial images by deep learning framework with feature fusion and context aggregation modules

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:32.316354Z

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Observation 7bc5cb99-03a1-4c8e-88f7-3f8af707898c · outbound

This paper cites Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks

Reference 15

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Observation 3167f28a-2498-4cf8-8180-da1d601f57df · outbound

This paper cites Semantic segmentation of agricultural images: A survey.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Semantic segmentation of agricultural images: A survey

Reference 16

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Observation 7a2a3e58-01bf-4749-a801-b6296a9cbe43 · outbound

This paper cites A convolutional neural network ap- proach for image-based anomaly detection in smart agriculture.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation A convolutional neural network ap- proach for image-based anomaly detection in smart agriculture

Reference 17

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Observation d1e35d7e-59b6-4cc8-a200-398a265f6c7a · outbound

This paper cites How can precision farming work on a small scale? a systematic literature review.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation How can precision farming work on a small scale? a systematic literature review

Reference 18

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Observation 4ccbb7f5-66db-4e90-8216-7a3d3ccd9ece · outbound

This paper cites A novel hybrid methodology integrating pixel-and object-based techniques for mapping land use and land cover from high-resolution satellite data.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation A novel hybrid methodology integrating pixel-and object-based techniques for mapping land use and land cover from high-resolution satellite data

Reference 19

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Observation 7faff049-de37-43a3-a682-62708f63e8d6 · outbound

This paper cites U-net: Convolutional networks for biomedical image seg- mentation.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation U-net: Convolutional networks for biomedical image seg- mentation

Reference 20

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Observation 0cd8d11a-0ee9-4d1f-986e-56411d0c34c6 · outbound

This paper cites Aaformer: a multi-modal transformer network for aerial agricultural images.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Aaformer: a multi-modal transformer network for aerial agricultural images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:30.663249Z

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Observation ac695f9a-fe96-424d-8270-58282f69b579 · outbound

This paper cites Effective data fusion with generalized vegetation index: Evidence from land cover segmentation in agriculture.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Effective data fusion with generalized vegetation index: Evidence from land cover segmentation in agriculture

Reference 22

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This paper cites Nawaz, Asghar Ali Shah, Saim Rasheed, Sheeba Ilyas, and Muhammad Khurram Ehsan.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Nawaz, Asghar Ali Shah, Saim Rasheed, Sheeba Ilyas, and Muhammad Khurram Ehsan

Reference 23

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Observation e3bd1aa1-77f9-4bd0-b197-141deb6a0a11 · outbound

This paper cites Ecaseg: Enhancing semantic segmentation with edge context and attention strategy.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Ecaseg: Enhancing semantic segmentation with edge context and attention strategy

Reference 24

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Observation c46ea817-c585-4c88-bb97-0057dd3e3f51 · outbound

This paper cites Augmentation invariance and adaptive sampling in semantic segmentation of agricultural aerial images.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Augmentation invariance and adaptive sampling in semantic segmentation of agricultural aerial images

Reference 25

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Observation 8290a1b2-122c-4683-9fef-cbd46cb7e651 · outbound

This paper cites Reslmffnet: a real-time semantic segmentation network for precision agriculture.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Reslmffnet: a real-time semantic segmentation network for precision agriculture

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:29.560219Z

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Observation 39628ec6-c919-4aa3-8493-051d577aa7be · outbound

This paper cites Attention is all you need.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Attention is all you need

Reference 27

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Observation cd60b43f-f7fc-4fe1-8d6d-ffd739cbcbd1 · outbound

This paper cites Np-semiseg: when neural processes meet semi-supervised semantic segmentation.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Np-semiseg: when neural processes meet semi-supervised semantic segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:29.309098Z

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Observation 8f28a2f4-14a3-4829-b862-a764acbda78a · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers

Reference 29

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Observation 40e0cde2-603b-4bcc-8006-97f7ed028bd1 · outbound

This paper cites Alvarez, and Ping Luo.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Alvarez, and Ping Luo

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:29.050673Z

Source-reported events for the cited work

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Observation 4f811d77-dd45-4934-9fd4-20689c576bf2 · outbound

This paper cites Reducing the feature divergence of rgb and near- infrared images using switchable normalization.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Reducing the feature divergence of rgb and near- infrared images using switchable normalization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:28.761664Z

Source-reported events for the cited work

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Observation 8e2f6c47-8e42-40ad-82af-240fe0878f45 · outbound

This paper cites Netadapt: Platform-aware neural network adaptation for mobile applications.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Netadapt: Platform-aware neural network adaptation for mobile applications

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:08:28.562400Z

Source-reported events for the cited work

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Observation 9861db46-f35b-402f-a3c2-108793a6a5db · outbound

This paper cites Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models.

Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:08:28.055860Z

Source-reported events for the cited work

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Pith citing papers

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