{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YLGGZZER7E4RDOTGLQZWNJOPIS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"052a6d890b4ccdf909a1f19328b079c5b05e9878f04f3c749c08673a45ee6a48","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-02T22:36:07Z","title_canon_sha256":"9aaab2c5cd93b8805b32aa478f646e3ae7d95838738c6185a2e7d0f5322fa5e3"},"schema_version":"1.0","source":{"id":"2108.01200","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.01200","created_at":"2026-07-05T04:49:21Z"},{"alias_kind":"arxiv_version","alias_value":"2108.01200v3","created_at":"2026-07-05T04:49:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.01200","created_at":"2026-07-05T04:49:21Z"},{"alias_kind":"pith_short_12","alias_value":"YLGGZZER7E4R","created_at":"2026-07-05T04:49:21Z"},{"alias_kind":"pith_short_16","alias_value":"YLGGZZER7E4RDOTG","created_at":"2026-07-05T04:49:21Z"},{"alias_kind":"pith_short_8","alias_value":"YLGGZZER","created_at":"2026-07-05T04:49:21Z"}],"graph_snapshots":[{"event_id":"sha256:6e559eba8c3a4c66d453e5424cbdee415f229cc3e943286dc6aaa9ae7179c654","target":"graph","created_at":"2026-07-05T04:49:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2108.01200/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a ","authors_text":"C. Premebida, C.S.S. Ferreira, G. Gon\\c{c}alves, M. Monteiro, P. Conde, T. Barros, U.J. Nunes","cross_cats":["cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-02T22:36:07Z","title":"Multispectral Vineyard Segmentation: A Deep Learning approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.01200","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2467a2fb5ed536f3f35a9a64dc9f5bfbfbaeac23ee38ab2ffedc038719038634","target":"record","created_at":"2026-07-05T04:49:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"052a6d890b4ccdf909a1f19328b079c5b05e9878f04f3c749c08673a45ee6a48","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-08-02T22:36:07Z","title_canon_sha256":"9aaab2c5cd93b8805b32aa478f646e3ae7d95838738c6185a2e7d0f5322fa5e3"},"schema_version":"1.0","source":{"id":"2108.01200","kind":"arxiv","version":3}},"canonical_sha256":"c2cc6ce491f93911ba665c3366a5cf44bf58c268cb7480f8f93327fe6110a92c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2cc6ce491f93911ba665c3366a5cf44bf58c268cb7480f8f93327fe6110a92c","first_computed_at":"2026-07-05T04:49:21.139355Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:49:21.139355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"onlnH5ibeKobMciqF9xNUzrh9xHTw/UeVKkJulgU+tIWd93ldf6SkgtNLvBPfXElEyAErjDiITJO6E49zqNyDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:49:21.139775Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.01200","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2467a2fb5ed536f3f35a9a64dc9f5bfbfbaeac23ee38ab2ffedc038719038634","sha256:6e559eba8c3a4c66d453e5424cbdee415f229cc3e943286dc6aaa9ae7179c654"],"state_sha256":"bd8c7091ca719533f9f7e81147158c54ca825fd399898e8c2766722ad707fc91"}