Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:38:48.144150Z
Paper Citation Record · LEDGER
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world
As of 21 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2505.09278.
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 2026-08-21T06:32:19.484+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
20 of 20 outbound references displayed
- verified exact2
- verified fuzzy16
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 4f9293ac-ae16-47cc-a19a-d47a90b3d53f · outbound
This paper cites Drones in Precision Agri- culture: A Comprehensive Review of Applications, Technologies, and Challenges,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Drones in Precision Agri- culture: A Comprehensive Review of Applications, Technologies, and Challenges,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:49.006055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0b639a37-0447-4ab0-bb80-0ff8d30af8a2 · outbound
This paper cites A survey on deep learning-based identification of plant and crop diseases from UA V-based aerial images,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world A survey on deep learning-based identification of plant and crop diseases from UA V-based aerial images,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.989830Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 9ee81425-0dfe-45a0-98ff-f9361502dc08 · outbound
This paper cites An automatic method for weed mapping in oat fields based on UA V imagery,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world An automatic method for weed mapping in oat fields based on UA V imagery,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.973814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1a0010ae-ab1c-4b08-a968-30799f4c15b2 · outbound
This paper cites Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics?.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics?
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.892090Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8aa9d074-9231-4163-90b5-b387f4063e34 · outbound
This paper cites Drones in agri- culture: A review and bibliometric analysis,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Drones in agri- culture: A review and bibliometric analysis,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.819885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fc337ef0-5d57-4a28-9eba-9609b914a895 · outbound
This paper cites Extent and Implications of Weed Spatial Variability in Arable Crop Fields,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Extent and Implications of Weed Spatial Variability in Arable Crop Fields,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.803094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1ff080c4-3e79-4982-b030-6eb352a0054e · outbound
This paper cites The Spatial Analysis of Soilborne Pathogens and Root Diseases,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world The Spatial Analysis of Soilborne Pathogens and Root Diseases,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.785247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation f2bb03ae-b61b-43f2-8ec1-8c5dfae16773 · outbound
This paper cites Learning- based methods for adaptive informative path planning,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Learning- based methods for adaptive informative path planning,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.769027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2b908646-df68-4d46-9751-f598b23de2f5 · outbound
This paper cites an unresolved cited work.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T21:38:48.702292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4d577e08-71e3-4143-8ff0-122c0e2aba2f · outbound
This paper cites Drone Deep Reinforcement Learning: A Review,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Drone Deep Reinforcement Learning: A Review,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.673812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 20ebf693-480c-45b7-879e-34d2dd481b31 · outbound
This paper cites UA V Path Planning and Obstacle Avoidance Based on Reinforcement Learning in 3D Environments,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world UA V Path Planning and Obstacle Avoidance Based on Reinforcement Learning in 3D Environments,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.656370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 421fd829-99ad-4cb4-aae5-8cccbacc9433 · outbound
This paper cites UAV-based path planning for efficient localization of non-uniformly distributed weeds using prior knowledge: A reinforcement-learning approach.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world UAV-based path planning for efficient localization of non-uniformly distributed weeds using prior knowledge: A reinforcement-learning approach
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T21:38:48.312951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 15492b75-ef46-4be2-ad52-c03d05eb9023 · outbound
This paper cites Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.639356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2cb9a3f2-c2f0-462d-97d8-543cf37bf5d0 · outbound
This paper cites UA V Path Planning using Global and Local Map Information with Deep Reinforcement Learning,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world UA V Path Planning using Global and Local Map Information with Deep Reinforcement Learning,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.582962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 65801bc1-10a4-4f74-a5bb-e27a23d9ea23 · outbound
This paper cites Human-level control through deep reinforcement learning,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Human-level control through deep reinforcement learning,
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:38:48.118014Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4488fdad-41cb-4a91-88a6-657108458902 · outbound
This paper cites Ultralytics YOLO,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Ultralytics YOLO,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.523240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 88fa4513-98cf-49d5-9f50-f8a637c3152d · outbound
This paper cites Metashape Professional,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Metashape Professional,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.497694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 77a5aa69-cb5b-4258-8577-5dfae570dab0 · outbound
This paper cites Adaptive path planning for efficient object search by UAVs in agricultural fields.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Adaptive path planning for efficient object search by UAVs in agricultural fields
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T21:38:48.234713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fef42475-e6e5-43b4-86b6-ae1efa01136f · outbound
This paper cites Fields2Cover: An open-source coverage path planning library for unmanned agricultural vehicles,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world Fields2Cover: An open-source coverage path planning library for unmanned agricultural vehicles,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.421160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 704217df-d6d7-4039-9275-b597873cff26 · outbound
This paper cites A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications,.
A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:38:48.404599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.