{"as_of":"2026-08-05T01:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:866f56867f2a704cc0dc1e5b5ca08e4aeefa4e9608c84508c27a60824eb3c265","coverage":[{"denominator":148,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T15:02:40.078574Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.18576/citation-record","integrity":"/paper/2607.18576/integrity","json":"/paper/2607.18576/citation-record.json","paper":"/paper/2607.18576"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/pdb.emo105","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:26.417867Z","title":"Tomato (solanum lycopersicum): A model fruit-bearing crop","venue":null,"work_id":"2b1c6a2f-9ffd-4af8-ac56-a1b80e06552d","year":2008},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:28.628960Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:bfdd0a262e5a0a483903ea305acad29b018f16024e17d4216623ccb20fb19884","observation_id":"b001cde1-23ad-4f13-bc15-03269449cf21","resolution":{"observed_at":"2026-08-01T15:03:26.484814Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00122-013-2066-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:26.333865Z","title":"Cobb, Genevieve DeClerck, Anthony Greenberg, Randy Clark, and Susan McCouch","venue":null,"work_id":"1d7ec92e-350c-48b0-adc0-39b448c9c0e8","year":2013},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:28.826423Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:112d4a413ea380151d617b2ea78ea427346bead9e3910d562eeea801655a970a","observation_id":"a2246ea2-8423-42f4-9189-0ca4fdee3271","resolution":{"observed_at":"2026-08-01T15:03:26.400292Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:29.207660Z","title":"Quantitative extraction and evaluation of tomato fruit phenotypes based on image recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.207660Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:833774189ba325e3afefa08ac03e45fdbd4ab507c2552ed023216b0201c9755d","observation_id":"249feb51-d195-4985-a6dc-2f25ea9b3a6d","resolution":{"observed_at":"2026-08-01T15:02:29.207660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41597-026-06926-9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:26.199047Z","title":"Tomato multi-angle multi-pose dataset for fine-grained phenotyping","venue":null,"work_id":"6da374f5-bd3c-4005-8bbf-dff221097767","year":2026},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.363636Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:2bcf681ff8c4dabae3c6d46091ae70c3c5e08e946bac1a8a4b45e18fa7027e0e","observation_id":"75d77124-b9b7-400b-9771-d5471c5d1aad","resolution":{"observed_at":"2026-08-01T15:03:26.246602Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:29.512934Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.512934Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:cc73c98ef1262e25724d6379ee076d1a6e9427161aff589ddb38739435dc8631","observation_id":"bc1280fc-41e1-4695-866a-78f7c6af2088","resolution":{"observed_at":"2026-08-01T15:02:29.512934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/agronomy12081865","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:26.124903Z","title":"3dphenomvs: A low-cost 3d tomato phenotyping pipeline using 3d reconstruction point cloud based on multiview images","venue":null,"work_id":"29f82c57-1c12-4fdf-a9b9-e3544ef5e5d5","year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.607125Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:7dd621c636050c5396a75480cf2b94bd098e6d35eb90e28c7fd9f608df3118c1","observation_id":"4ee26557-d671-42d8-9bc9-16f72c69d7b2","resolution":{"observed_at":"2026-08-01T15:03:26.188462Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11119-020-09738-y","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.958096Z","title":"White, J","venue":null,"work_id":"ac843688-181a-4b9e-9e2e-9856a2b52b19","year":2021},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.745929Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:cc052fdfe9822784cb8e9576346ed8e3e9ec461a3ab912b3f4d95c0a8e962a3f","observation_id":"2df989f7-8195-4ed5-bf57-b3e8bde83180","resolution":{"observed_at":"2026-08-01T15:03:26.001096Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:29.832953Z","title":"Michels, Soren Pirk, Chia-Chun Fu, and Wojciech Palubicki","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.832953Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:1e4ac9677bcb7ab304276951b1bab0b530888624ddc0188de616b42c2d806b63","observation_id":"e44518a7-281a-41c3-aca5-9275885d1bd5","resolution":{"observed_at":"2026-08-01T15:02:29.832953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:29.923857Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.923857Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:3f5275b001c889fb43fbc9c29d716acd39ee1a9772a5ee452f6414fda74e81c0","observation_id":"f7b70881-4cf9-4b00-9984-343f054afb19","resolution":{"observed_at":"2026-08-01T15:02:29.923857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:29.996730Z","title":"Effectiveness of training with procedurally generated synthetic images of crop plants","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:29.996730Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:0d6246dfa2b6956a32d7207f14e7f1dde9459b6d216c1e2cc2c4172823140d0e","observation_id":"cff1b76e-ebff-4962-b908-71429bde5a7e","resolution":{"observed_at":"2026-08-01T15:02:29.996730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.059349Z","title":"Winsyn: A high resolution testbed for synthetic data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.059349Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:e255a5b5632698f68a1007fc8fa2ccc51a4b6377454900e617cdf14bd1212aeb","observation_id":"aa377feb-896d-4c35-9c43-c9d6eeb356ef","resolution":{"observed_at":"2026-08-01T15:02:30.059349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.129502Z","title":"Bhattacharyya","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.129502Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:6aeedde1ece4192565539af61e44d9bd7a95d728d75f4e11929d44dfecec192f","observation_id":"eb2b3917-2ab4-4269-bd04-65310c5f2149","resolution":{"observed_at":"2026-08-01T15:02:30.129502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.207918Z","title":"Sam 3: Segment anything with concepts, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.207918Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:4f49de76a4a156c6b888dd0272bf661c41e149d4388707c85ec541f8c592958f","observation_id":"10f14b9a-3df7-42cf-a9c0-6650980766b0","resolution":{"observed_at":"2026-08-01T15:02:30.207918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11633-023-1385-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.843011Z","title":"Segment anything is not always perfect: An investigation of sam on different real-world applications","venue":null,"work_id":"f658ddba-178a-4c06-aeb5-aa279b207781","year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.286955Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:6de86e33709732dd9a307cb1f46d4553a19bfd366b6c5a0e2bfaa5e919b713d0","observation_id":"769d51c0-b972-4bfe-a7e3-41c489805414","resolution":{"observed_at":"2026-08-01T15:03:25.940012Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[{"edge_observation":{"observed_at":"2026-08-01T18:19:51.591462+00:00","source":"paper_reference_links","state":"open"},"event_date":"2024-09-12","event_type":"correction","notice_doi":"10.1007/s11633-024-1526-0","provenance":{"observed_at":"2026-07-11T03:08:16.498698+00:00","source":"crossref","source_record_id":"10.1007/s11633-024-1526-0->10.1007/s11633-023-1385-0:correction"}}],"reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.434302Z","title":"Tsaftaris","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.434302Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:9f6f808a873637108417541fa2fa2943baf606e668837fb583a7d9eb27eddd4e","observation_id":"caa2c034-fdfa-4531-9ebd-3ea91245a5bb","resolution":{"observed_at":"2026-08-01T15:02:30.434302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/rs16020414","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.709521Z","title":"Evaluating the efficacy of segment anything model for delineating agriculture and urban green spaces in multiresolution aerial and spaceborne remote sensing images","venue":null,"work_id":"be9ae4d8-eada-456b-bf44-cafec6db9a7e","year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.511604Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:e45075ccaa55ae11a49e0a51e5c4754be8f6883598e991ec541fdd3dc7349678","observation_id":"6f18d19a-7813-4b7e-8503-681abbed8191","resolution":{"observed_at":"2026-08-01T15:03:25.831614Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.583009Z","title":"Agri-fm+: A self-supervised foundation model for agricultural vision","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.583009Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:a3b4d8e7ad39e2ebcfcb2e9f5378c56d506c876f580a2c7e4d7e1857a5f690f0","observation_id":"1bd8212b-1843-4e61-9510-2d9e39f04aa4","resolution":{"observed_at":"2026-08-01T15:02:30.583009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.649475Z","title":"Few-shot adaptation of grounding dino for agricultural domain","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.649475Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:e852496c98a533072a093e4b5f684061468ae8523cb66063ac1d7a0306943317","observation_id":"a3d11930-d41c-4f35-9715-a5ab743b8016","resolution":{"observed_at":"2026-08-01T15:02:30.649475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.730558Z","title":"Bailey, and J","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.730558Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:2310fcf4ec80363d156d7aa1fa71ed78d99408526bc71a7e854d346ae7455932","observation_id":"3491b875-3692-44bb-ad54-2ffd58420751","resolution":{"observed_at":"2026-08-01T15:02:30.730558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:30.782674Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.782674Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:ab84b86135c37cdbd2576851efadf1346c4c5ecc9fb2bdf8972026c22e914e67","observation_id":"7b1189ff-bef2-4ec0-89c1-c867df272ee0","resolution":{"observed_at":"2026-08-01T15:02:30.782674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s13007-018-0273-z","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.528612Z","title":"The use of plant models in deep learning: an application to leaf counting in rosette plants","venue":null,"work_id":"3d43799b-002c-4eab-9ae8-4aff863b5dd8","year":2018},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:30.965957Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:493341ed447103b349826dddb100775f7dd322afb13c5981a6bb9f4827d1e561","observation_id":"f815e048-01a2-4240-a755-bdbbef70c4e3","resolution":{"observed_at":"2026-08-01T15:03:25.692481Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.042938Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.042938Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:a2a4f2ac74ac25e244d835aebe60637fe832ae07c9780062703a20836c63dc4f","observation_id":"9075634f-07f6-4c58-9c8d-82d9c2b93ea4","resolution":{"observed_at":"2026-08-01T15:02:31.042938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.112598Z","title":"Schwing, Robert Brunner, Hrant Khachatrian, Hovnatan Karapetyan, Ivan Dozier, Greg Rose, David Wilson, Adrian Tudor, Naira Hovakimyan, Thomas S","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.112598Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:d997ad479a4ae59956819ccd56529213abb6ddabcf41d88873256b3195d077de","observation_id":"5ccd3e88-77e2-49ff-a3af-c962375a9d83","resolution":{"observed_at":"2026-08-01T15:02:31.112598Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41598-018-38343-3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.387980Z","title":"Konovalov, Bronson Philippa, Peter Ridd, Jake C","venue":null,"work_id":"8e82d0e0-9749-46c1-b4da-7884c2466efb","year":2058},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.198005Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:4c0b80abca55b49fddf7bd868ca3e9baf571239c9f90dd64d1483ab2df3ce750","observation_id":"810668a5-921b-489d-a559-8dfe9802ef30","resolution":{"observed_at":"2026-08-01T15:03:25.523862Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.280086Z","title":"A realistic synthetic mushroom scenes dataset","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.280086Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:f24979063c800f82db42283ed14f7b8843ef5f3522bf6bfdc2b55ceab3619350","observation_id":"544eba5b-233e-4faf-99f2-85405e80b456","resolution":{"observed_at":"2026-08-01T15:02:31.280086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/s19051058","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.309709Z","title":"Cropdeep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture","venue":null,"work_id":"183f09d1-ea71-4436-b3a9-17c7a9bdafdc","year":2019},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.345323Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:79d9ea9a5b7a7478e26809dad17aa42a39725fa601ed4a8ff003e99b2ec9360e","observation_id":"c3a891a2-ee6c-471b-82fa-798d61391f69","resolution":{"observed_at":"2026-08-01T15:03:25.380941Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.433045Z","title":"Deep convolutional neural networks for image-based convolvulus sepium detection in sugar beet fields","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.433045Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:4704a37b5ccc9a296112f84e75add1b6c5555dde827eae4c94683b0c83d71537","observation_id":"3fc9cb77-f4c4-4e04-bea1-58dfa58883a5","resolution":{"observed_at":"2026-08-01T15:02:31.433045Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.535066Z","title":"Domain generalization for crop segmentation with standardized ensemble knowledge distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.535066Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:5463db76b06f42e17e1240dc0706f92b06f35d54010bc07f5ef317ebc390a9b2","observation_id":"e4513896-ae14-410c-a9fe-ca687ccbbede","resolution":{"observed_at":"2026-08-01T15:02:31.535066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.705004Z","title":"Plantdreamer: Achieving realistic 3d plant models with diffusion-guided gaussian splatting","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.705004Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:f1dcccf1a4cf35439da4dcb10db7ee214b72070515bef9404f6af156fee7f29b","observation_id":"ffc20506-c36b-44aa-a7e2-ed20a2b82eb5","resolution":{"observed_at":"2026-08-01T15:02:31.705004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41598-022-23399-z","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.149247Z","title":"Sapkota, Sorin Popescu, Nithya Rajan, Ramon G","venue":null,"work_id":"5bc0149e-aba8-47d0-bebb-fecf75a5215a","year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.831548Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:2bdf2ab46e8ba671a7fed222956b04e0c877d8921fef32809a5719875538ea4b","observation_id":"e889bfdf-558a-4ee7-a382-e489eb6b2e1d","resolution":{"observed_at":"2026-08-01T15:03:25.249646Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.893253Z","title":"Synthset: Generative diffusion model for semantic segmentation in precision agriculture","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.893253Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:a145f617c8b7ad43e6eb8be1fea8a02937f0bb1f8dbf621efa16b6b881bc9527","observation_id":"026dc028-4874-49b6-8bd4-d59bfc642b1d","resolution":{"observed_at":"2026-08-01T15:02:31.893253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:31.972470Z","title":"Beyond annotations: Efficient wheat head segmentation using l-systems, game engines, and student-teacher models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:31.972470Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:d7d9a7105316b95b58edd096744dc22efd8d3f8080b856f7d31170915a101348","observation_id":"cf6dd4da-1e35-46a7-9517-3c02eb600345","resolution":{"observed_at":"2026-08-01T15:02:31.972470Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/jimaging10070152","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:25.014378Z","title":"Efficient wheat head segmentation with minimal annotation: A generative approach","venue":null,"work_id":"293a519b-c8ae-4d18-b344-36b011853db8","year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:32.139109Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:4411cd36cf791aac3755d84c73bfb1c16b1bb2412561d247cc852424e349d331","observation_id":"b9ca5bd6-89fe-49db-8f8d-efbc03537a42","resolution":{"observed_at":"2026-08-01T15:03:25.086879Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:32.356874Z","title":"A dataset for semantic and instance segmentation of modern fruit orchards","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:32.356874Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:84f257357ad795fd5bce03d182b7ade3a083b209f3cdf1dda3570dde29c9c86f","observation_id":"9ad2b791-ba58-4e93-9f62-5530040b8ff1","resolution":{"observed_at":"2026-08-01T15:02:32.356874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:32.573380Z","title":"De Visser, Gerie van der Heijden, and Gerhard Buck-Sorlin","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:32.573380Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:1cdafcd8f510dd0882bd00a8fb28f8966675818611e9bf8327949a84fb9b627d","observation_id":"42fe941d-6c2a-44cb-b5f1-639283a5a8c3","resolution":{"observed_at":"2026-08-01T15:02:32.573380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/insilicoplants/diaf024","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:23.773014Z","title":"A functional–structural plant model for dwarf tomato ideotype identification in vertical farming","venue":null,"work_id":"5299b04a-904c-44e4-bfb6-b6d302520062","year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:32.825842Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:30b77ea8aef30354497720cddaa000f7a7ce1115f08debf9403104c7e1e49afd","observation_id":"10b395fd-9028-4c1c-a5e2-5187623906db","resolution":{"observed_at":"2026-08-01T15:03:23.919941Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:32.938601Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:32.938601Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:a5cca4536774e71320dab5dced2cb8cadc9b71db947b96a81448748c4cec53ee","observation_id":"028e682d-982b-4825-8016-3aec9ade5aae","resolution":{"observed_at":"2026-08-01T15:02:32.938601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:33.240671Z","title":"LaboroTomato : Instance segmentation dataset","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:33.240671Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:ddd6699d89abe379f3a4c35e8d797a0c77d6ab6648b6b3e4c725c8c341db9450","observation_id":"a617f16d-4535-4f5e-8c5d-e0a41dd71c36","resolution":{"observed_at":"2026-08-01T15:02:33.240671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:33.354491Z","title":"Tomato fruit detection and counting in greenhouses using deep learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:33.354491Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:d8deb688faa84988e7c92dbc5b38caa6b6b59fbac429f8ef4cf90a95b7678842","observation_id":"96bd3d25-1bd6-4369-bd5e-5867260ef3bf","resolution":{"observed_at":"2026-08-01T15:02:33.354491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:33.465211Z","title":"Tsironis, S","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:33.465211Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:05e1a3be353d90e37e0a1b3237eb5e262da7277a604fa73a2cb3b32d3d29c944","observation_id":"0fd5661a-9da7-45c2-bcbc-3f9d45ae36c3","resolution":{"observed_at":"2026-08-01T15:02:33.465211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/agronomy12020356","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:23.484627Z","title":"Benchmark of deep learning and a proposed hsv colour space models for the detection and classification of greenhouse tomato","venue":null,"work_id":"7b2e05b9-2c1f-4f14-99ce-f62347eb194c","year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:33.577466Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:b43af5c630d78c16fb82ee11ae967637e5642609aeaff861154676f4c3b54867","observation_id":"0faafa52-212d-40e1-bc93-b67230cd50c0","resolution":{"observed_at":"2026-08-01T15:03:23.645165Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:33.791714Z","title":"The Algorithmic Beauty of Plants","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:33.791714Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:9b1a7492d29f98161c391db64bd5964d8316229953a13939b71341adbef0059f","observation_id":"138c1be8-f370-4281-84b4-76db884780c2","resolution":{"observed_at":"2026-08-01T15:02:33.791714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:33.908102Z","title":"M e ch and P","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:33.908102Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:f771a19da26ba46dd23070c0137b747af40c7427ebfb659f5f063691c61ff26e","observation_id":"ccf20faa-6fb6-4661-a67b-cb9a81f80ba1","resolution":{"observed_at":"2026-08-01T15:02:33.908102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:34.181655Z","title":null,"venue":null,"work_id":null,"year":1944},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:34.181655Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:7ab860d0f6a92b0a10bc1a48d7568b473f77d9b63f7cd4942a3bc6dc08a92a9d","observation_id":"07fa59c1-b13b-4879-b0b6-1356a10460d9","resolution":{"observed_at":"2026-08-01T15:02:34.181655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:34.328650Z","title":"Torres Quezada","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:34.328650Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:bdc3215c3e7076e34b4cce1b4344abc1860c8becd8ac32d43ec1253a7b8ecb8f","observation_id":"6f5dd813-e745-459d-8a37-e109b7cecdb6","resolution":{"observed_at":"2026-08-01T15:02:34.328650Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:34.482471Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:34.482471Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:440f115b0b0a835ce9f34a2a8ab5b2eb7012b9f843d8db9767876bb54d08dbe3","observation_id":"7566f038-3bee-420d-8ba5-706b516f2e99","resolution":{"observed_at":"2026-08-01T15:02:34.482471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:34.599954Z","title":"Robust fine-tuning of zero-shot models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:34.599954Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:4b5dbc0bf02c32cdda68c4c0a985700587afa5c7c486721be567b72c2455e2a9","observation_id":"94177677-cefc-44d4-b0a9-8499cf90ee7c","resolution":{"observed_at":"2026-08-01T15:02:34.599954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:34.771322Z","title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:34.771322Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:bf9bee04768f7d44bf109945e7e4aaaef71bf52257e77a2e9174a3a11ca0cf31","observation_id":"431bf8a7-56db-434c-bf58-35aaa2f6e24e","resolution":{"observed_at":"2026-08-01T15:02:34.771322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:34.893038Z","title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:34.893038Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:2d56a49e5e761f171b4082e36db9df1130395d3f5fe6f1077f2194b8efd2b98d","observation_id":"81fa0573-a510-44c9-a957-64640ed6c994","resolution":{"observed_at":"2026-08-01T15:02:34.893038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:35.004981Z","title":"Segformer: Simple and efficient design for semantic segmentation with transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:35.004981Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:fb24c56d010b0db4e9e95f096fb2fcae9597574390f651cd53c73c57c080919d","observation_id":"b819eaeb-0dc6-4e6c-b045-11a51cf5dc59","resolution":{"observed_at":"2026-08-01T15:02:35.004981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:35.156985Z","title":"Your ViT is Secretly an Image Segmentation Model","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:35.156985Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:0a60c9469bb3ef5a18e72803b2ae06265dc981aa7f8c2f59da360cac4dd2c91b","observation_id":"c1d760c9-ff54-4dc0-8b19-38ed6e0f8919","resolution":{"observed_at":"2026-08-01T15:02:35.156985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:35.419075Z","title":"Zero-shot hierarchical plant segmentation via foundation segmentation models and text-to-image attention","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:35.419075Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:76767121567069687e3a49ad5aa550473b4395340e6a167c84cee105d6ec94c2","observation_id":"0607ea05-ff53-4f0c-9ed0-e0567a23b521","resolution":{"observed_at":"2026-08-01T15:02:35.419075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00138-015-0728-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:22.927432Z","title":"An opinion on imaging challenges in phenotyping field crops","venue":null,"work_id":"bba8f4eb-57b2-4a68-9dd6-e156f429ffd3","year":2016},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:35.591167Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:045eaaa993d207182ca35efa53c58b3b57a980b7d3c606ee550e9ae4023537e7","observation_id":"5529298a-3e68-4559-93f8-85d1891d7bfe","resolution":{"observed_at":"2026-08-01T15:03:23.050735Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:35.710949Z","title":"Recognition and localization methods for vision-based fruit picking robots: A review","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:35.710949Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:b815220ef89869511a651c71ec5e4922811d4e93e676b57ab6ae2c0a3f86a91b","observation_id":"88898684-290a-4b02-92e7-93a07fe89dc0","resolution":{"observed_at":"2026-08-01T15:02:35.710949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/aob/mcz205","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:22.661316Z","title":"Quantifying the importance of a realistic tomato (solanum lycopersicum) leaflet shape for 3-d light modelling","venue":null,"work_id":"f975e22b-2b85-4a6c-95cb-6a4dddbb28c0","year":2019},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:35.831992Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:368770e66d0fc2785b85f7157c092524bbad97157c22292043cccf62712d3052","observation_id":"7865f9ac-cc89-475b-a0dd-5b69f557e0cc","resolution":{"observed_at":"2026-08-01T15:03:22.792692Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/insilicoplants/diaf022","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:22.415783Z","title":"Development of a tomato functional–structural plant model for digital twin applications","venue":null,"work_id":"04ef7133-a839-4efc-b89a-3e26fbaae596","year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:35.950383Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:1623c77278ab519519bcf6744f4327e3bae3887cb8ebc46e66bc753f7297aead","observation_id":"6a14d61f-e6c3-4c84-b91e-693f83b8c792","resolution":{"observed_at":"2026-08-01T15:03:22.535011Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.047198Z","title":"Xfrog - procedural organic 3d modeler [3d models]","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.047198Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:41c8a294eb5ffa686f3b64bf50ad5142f397f09bebf102d8c63e483303ebf685","observation_id":"b66ad85e-0317-4a04-8f3a-13e176fbf2b6","resolution":{"observed_at":"2026-08-01T15:02:36.047198Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.138416Z","title":"Prusinkiewicz, R","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.138416Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:11b1395e9c85d41c228b41d2d8b3e5f8fd238ac48202c1979be4744bb1d860fa","observation_id":"dd7570aa-04d7-43ab-a8bd-f5e914779a55","resolution":{"observed_at":"2026-08-01T15:02:36.138416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.223299Z","title":"The use of positional information in the modeling of plants","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.223299Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:562fad5e00bc565b1546e15b4bc3758c2ab4e53b9f541da32b6d9027fc63bdbb","observation_id":"24ea3e13-fcdc-4972-bbae-e8a4cdfce9b6","resolution":{"observed_at":"2026-08-01T15:02:36.223299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.283302Z","title":"Mohanty, David P","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.283302Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:3233b2fd97a9e367c1200315d78354823e77c7dc43cd2498056e769bd6e67f1a","observation_id":"60c4584f-1e25-4b3e-94a8-d429ba65e777","resolution":{"observed_at":"2026-08-01T15:02:36.283302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.368247Z","title":"COSYS-AIRSIM : A real-time simulation framework expanded for complex industrial applications","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.368247Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:93f40e7cfa1c10da78ea590a316ce9e3a97706dda6b5422c13c090c44f7a3c52","observation_id":"7da26fb0-1f23-42ab-ade1-cf9c8802de6f","resolution":{"observed_at":"2026-08-01T15:02:36.368247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.436672Z","title":"in silico Plants , volume =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.436672Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:9031e5cf52150444bd438b8ac4b7dd00ed93a542dbc11ab918b4b75504ac69cf","observation_id":"60cdcd9b-03c6-41b9-8d11-ce012d35da39","resolution":{"observed_at":"2026-08-01T15:02:36.436672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.512578Z","title":"and Pirk, Soren and Fu, Chia-Chun and Palubicki, Wojciech , title =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.512578Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:a6ffe807409704cb51e117e49f47115ff2b5cf410cd04febaec40d760a095595","observation_id":"e5ec5683-b90c-479a-94c6-34c20a8076a9","resolution":{"observed_at":"2026-08-01T15:02:36.512578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.594550Z","title":"Graph-Grammars and Their Application to Computer Science , pages =","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.594550Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:16f16303d81aa2642b2aba63e2497cc24495b1165beaef9808aebdcee2384df5","observation_id":"3287d31d-d32e-4f00-bad5-5f06ce3ca575","resolution":{"observed_at":"2026-08-01T15:02:36.594550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.692831Z","title":"and Lindenmayer, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.692831Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:6b1d94000b326a3d053981765c3c5bf4407359a267094d4932c91ad0b0027b71","observation_id":"9d248c00-fba3-40e8-b766-58a63c2aa0f0","resolution":{"observed_at":"2026-08-01T15:02:36.692831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.757999Z","title":"The Algorithmic Beauty of Plants , publisher =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.757999Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:a9403d867f1ed9c13db9d887b2bf12666f61fcc4fb5314f9ccb93c3b4ec54261","observation_id":"a129c48a-6ea3-4dd6-920b-a3ce0fb8e2db","resolution":{"observed_at":"2026-08-01T15:02:36.757999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.820667Z","title":"and Mjolsness, Eric , title =","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.820667Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:a954e9efb4373a0716f0183d14a91eb548f930cee221fac89ec1fdde7cd14212","observation_id":"13c9d527-6012-4f15-884e-6f25b8039234","resolution":{"observed_at":"2026-08-01T15:02:36.820667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.885957Z","title":"The Use of Positional Information in the Modeling of Plants , booktitle =","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.885957Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:8e180460a87b858a363e8ffec4c03b053862471a44da05a1f07b2590033c3131","observation_id":"ddb462a7-b47f-4958-a842-2fba071c672f","resolution":{"observed_at":"2026-08-01T15:02:36.885957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:36.966819Z","title":null,"venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:36.966819Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:80d5c725be99a3a55ead39f923522ce694f0ffba57733a97ba0f6355f2a26d2b","observation_id":"4c3bb005-f23a-4dc2-9381-0c7c6f2f0091","resolution":{"observed_at":"2026-08-01T15:02:36.966819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.059999Z","title":", title =","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.059999Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:ae8b58fab39b592976ad114e6fe78102ac693f8c9b7d4bf4deb9c81e30e051e6","observation_id":"d9ca756d-a325-4760-a07b-5e942da0a7fc","resolution":{"observed_at":"2026-08-01T15:02:37.059999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.145609Z","title":"Prusinkiewicz and R","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.145609Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:2c77ec5832d643785f000c0b7c171162219efa4455e70171b2e47fd11b97d748","observation_id":"c89798d4-0ad3-400f-871f-b14d9e82bce2","resolution":{"observed_at":"2026-08-01T15:02:37.145609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.229189Z","title":"Mercer, P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.229189Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:2a59138363b4435bc0cd9080d6fc3b1173b2529207fe434745a06469f816bac2","observation_id":"ea0ab968-3d06-4604-b549-c949d069244f","resolution":{"observed_at":"2026-08-01T15:02:37.229189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.320422Z","title":"Frontiers in Plant Science , author =","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.320422Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:c615547c78c265d81530c826f5eb98d420ccb3134a2129a6c0ca06485789d724","observation_id":"fc2370a7-6d76-4760-ada8-08a915058dcc","resolution":{"observed_at":"2026-08-01T15:02:37.320422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.403855Z","title":"Annual Modeling and Simulation Conference (ANNSIM) , pages =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.403855Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:2446203e0c66cc39f351d111ddb36450aa81c47257b8b08355855689220dac62","observation_id":"afeea108-9126-47a7-a316-fcc9424ea561","resolution":{"observed_at":"2026-08-01T15:02:37.403855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/aob/mcr221","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:24.713503Z","title":"and de Visser, P","venue":null,"work_id":"ac7b1c47-f485-4642-a350-94fbef45662d","year":2011},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.490105Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:5f45b7342d304b4beb9cd476e01c046c50a1633ce05610c8d6f41a480c590a1f","observation_id":"d003777d-e209-4b9f-b2c7-b026b48da79f","resolution":{"observed_at":"2026-08-01T15:03:24.807030Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.575925Z","title":"Annals of Botany , volume =","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.575925Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:0c2685a344d8065a02304f8012c1dabd09b88693a423b4e66da80530a6f3f8f1","observation_id":"da1630ad-3ac7-449d-8f15-c56bb24358da","resolution":{"observed_at":"2026-08-01T15:02:37.575925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.renene.2020.06.144","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:24.077297Z","title":"2020 , issn =","venue":null,"work_id":"dce22582-cf40-41ca-ae14-ec3019e3ad04","year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.660001Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:afab868556d0202eb896192b2d7005b1edcec42d0af58b4ec6cc3b2c4b9e21fb","observation_id":"32329776-f262-4ac3-b1bf-32864e3100b5","resolution":{"observed_at":"2026-08-01T15:03:24.238376Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.752901Z","title":"in silico Plants , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.752901Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:c199b75c4352e6828f088ef19887ae7ca012717d6411c2f04457880396ff7d47","observation_id":"e5a20d3b-b9cf-4e00-91a8-2538e50a7835","resolution":{"observed_at":"2026-08-01T15:02:37.752901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:37.898369Z","title":"in silico Plants , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:37.898369Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:bcdea14d99fe0c3af811f365a0cd49b1d3f95c5e357e678bdbfa934f26c9b9c3","observation_id":"65073bc9-5f37-403c-adde-1dc27782df1d","resolution":{"observed_at":"2026-08-01T15:02:37.898369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:38.018021Z","title":"and van der Heijden, Gerie and Buck-Sorlin, Gerhard , title =","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.018021Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:e1477a11386179708e6ca5498e8344c7ddd7ee5be8d079863c7bdbbbcb399d18","observation_id":"fd3d7fe7-8a95-4810-991e-243aeeba131b","resolution":{"observed_at":"2026-08-01T15:02:38.018021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:38.130841Z","title":"2024 , eprint =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.130841Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:c22da1a1aa682ac34c3216aa14ec4eadea9687d90780f583a575faf4f3516a24","observation_id":"72da4ea4-9c42-463a-85ee-a5eb005cd8b9","resolution":{"observed_at":"2026-08-01T15:02:38.130841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:38.279947Z","title":"A Physically-inspired Approach to the Simulation of Plant Wilting , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.279947Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:24542eaafe7dce049ba4df8d5b90389a2d046fe98fdf15ffd67c7348c4ccb4a3","observation_id":"972e3d7b-4465-4671-bd2e-eed9c1cda322","resolution":{"observed_at":"2026-08-01T15:02:38.279947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:38.394360Z","title":"Agronomy , volume =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.394360Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:79590b621145627349299ef31a16f80774555638791bad037640710146f3bcf3","observation_id":"93572dd4-25d0-42bd-929f-7276b10f15ed","resolution":{"observed_at":"2026-08-01T15:02:38.394360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:38.514923Z","title":", title =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.514923Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:44d2ecbc4d592781885bb3bd15ac5477eb3fb080138aabfc35709b14848a9a43","observation_id":"b40e1bc7-ac17-4b23-9b81-2972585809bf","resolution":{"observed_at":"2026-08-01T15:02:38.514923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/insilicoplants/diab039","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:23.182087Z","title":"in silico Plants , volume =","venue":null,"work_id":"9867308c-eecb-4e96-b337-e13e7d0e9b46","year":2021},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.622437Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:b1661332687e14e9616ea5319e6230f0f747a346f289bcdf9e623901589ec443","observation_id":"f0b5eeee-52c0-4c41-81af-1ad17dfcc1ad","resolution":{"observed_at":"2026-08-01T15:03:23.362646Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:38.711715Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , month =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.711715Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:7abfef3469621571a117ed5a4aa9830d862f34e626b387f666a48ac2b738aa95","observation_id":"671b7a2b-84c7-4f26-9446-137d6e6733a4","resolution":{"observed_at":"2026-08-01T15:02:38.711715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:38.799435Z","title":"1999 , publisher =","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.799435Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:e1c02d35c58f71f683684a72b7eafe144a46e0f5e4f21c29357f94d41dc2c1d5","observation_id":"78e8dbde-9469-431b-ac5c-2511839aeae8","resolution":{"observed_at":"2026-08-01T15:02:38.799435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/jxb/eru356","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:24.419653Z","title":"Journal of Experimental Botany , volume =","venue":null,"work_id":"31d35391-6788-47fa-b921-1a2f29b647c7","year":2014},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":104,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:38.963685Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:18dc5fb6fb239fbe8f0d316ca6baaf36abb8a93b30975c647f90220101856267","observation_id":"e8ef7288-24d7-4188-b014-d52f37554596","resolution":{"observed_at":"2026-08-01T15:03:24.522582Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.050056Z","title":"and Bourou, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":105,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.050056Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:3831b554349551e6fd85d2c6a95ff4b4fe9fb974d72ebf9e51db62bd51445f50","observation_id":"bd606cc6-ffe2-42ea-9214-66347c8cdd35","resolution":{"observed_at":"2026-08-01T15:02:39.050056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.196127Z","title":"2020 , howpublished =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.196127Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:bbe8a9b69702a5f66303cfd2b332d3f450f72ff3fd3472417f65150e8942c9da","observation_id":"9eb840fd-5199-4d69-bf2c-a6dc0c76f5b2","resolution":{"observed_at":"2026-08-01T15:02:39.196127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.317387Z","title":"Sci Data , month =","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":107,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.317387Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:bbc2da0fbeaf3e4ec3bd84b6b445a358c446585bfa665e721f9427472c52da02","observation_id":"87dfab54-d299-44b1-a098-5c6729736e49","resolution":{"observed_at":"2026-08-01T15:02:39.317387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.450912Z","title":"2025 , doi =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":108,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.450912Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:cd995ce0614bfbddea8673e2d69a50be8307f5a5a00a5d84f6707004816ef51a","observation_id":"c661e37f-eaf2-4b20-9edb-0bcfb7e9f2cf","resolution":{"observed_at":"2026-08-01T15:02:39.450912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.556587Z","title":"and DeClerck, Genevieve and Greenberg, Anthony and Clark, Randy and McCouch, Susan , title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":109,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.556587Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:21728eee0a19d98f95010e6c3db51ad8ecc822c7aedbd3d551421953b417629c","observation_id":"ba958f33-5d7b-478d-800e-9aba60fd7aca","resolution":{"observed_at":"2026-08-01T15:02:39.556587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.670561Z","title":"2008 , doi =","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":110,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.670561Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:24fce2c991b3fcb8c767d6a9bbefd76fea760f0d997989ea120e9e41235dee6a","observation_id":"7d25519a-3f7d-461d-86c8-8558c3be1945","resolution":{"observed_at":"2026-08-01T15:02:39.670561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.739707Z","title":"Frontiers in Plant Science , volume =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":111,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.739707Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:f8ffaf25317d99e26f2d942199c6d2daf8ece2c6d928b580d4778b2063c3f88f","observation_id":"65758011-494b-4d7b-ad4e-c314b7e7fca5","resolution":{"observed_at":"2026-08-01T15:02:39.739707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/hr/uhaf109","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:03:26.008943Z","title":"Horticulture Research , volume =","venue":null,"work_id":"44fe3119-dfcc-4f4f-bbc7-7b90eb9dba3c","year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":112,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.811283Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:b7ba2eb2254e88d6a13f9f2dbecec4a060d52bc5f5e7a8be671a3a28a9448f31","observation_id":"a584ad92-9bf5-44e1-9b8a-82e527bd1ca4","resolution":{"observed_at":"2026-08-01T15:03:26.105507Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.884132Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":113,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.884132Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:3fa63931ed7c01f82d052e72b0f7d903904a4dbd903ae780fddfaadcd9aedc62","observation_id":"5590897d-2bf5-452d-99d6-211fb0f021ce","resolution":{"observed_at":"2026-08-01T15:02:39.884132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.942004Z","title":"2025 , eprint =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":114,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.942004Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:b976719c6fba83140ade10a72151a458fcfce60c6e734a6460f935ab4cf28edd","observation_id":"e75d073a-bf57-4f1c-bb00-0ec10c380796","resolution":{"observed_at":"2026-08-01T15:02:39.942004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:39.993656Z","title":"and Thomasson, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":115,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:39.993656Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:0afade83e06a50eaa961a80ebe57c0592c4fdc147107f6df9e51d4ceb5610ddf","observation_id":"d9a1d508-fedf-4fa4-85e5-d58b94ec6ca3","resolution":{"observed_at":"2026-08-01T15:02:39.993656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T15:02:40.078574Z","title":"International Conference on Learning Representations (ICLR) , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data","version":1},"reference_index":116,"source":"arxiv_source","source_observed_at":"2026-08-01T15:02:40.078574Z"},"links":{"citing_paper":"/paper/2607.18576"},"observation_digest":"sha256:e25718e3c98d0fd7bdcc51a745798d65b5abfc46622fead1d578801b7c27c749","observation_id":"821dca33-7f85-4b38-a252-05d14335cb37","resolution":{"observed_at":"2026-08-01T15:02:40.078574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.18576","last_updated":"2026-07-20T23:18:55Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-01T15:02:22.447222Z","submitted_at":"2026-07-20T23:18:55Z","title":"Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":78,"verified_exact":22,"verified_fuzzy":0},"total_outbound_references":148},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 100 of 148 outbound references and 0 inbound Pith citation observations for arXiv:2607.18576."}