{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CGXASZSCGOVIRZDXISS7SZMEWD","short_pith_number":"pith:CGXASZSC","schema_version":"1.0","canonical_sha256":"11ae09664233aa88e47744a5f96584b0dd563671447fa584e45b6722f26183b1","source":{"kind":"arxiv","id":"2105.07789","version":2},"attestation_state":"computed","paper":{"title":"Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Jana Kierdorf, Laura Verena Junker-Frohn, Lukas Drees, Ribana Roscher","submitted_at":"2021-05-17T13:00:01Z","abstract_excerpt":"Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach's core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, whi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2105.07789","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-17T13:00:01Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"4d07a87ba963ebaf2aca37c3b7d488c2d24cff1e034553864420660965df9808","abstract_canon_sha256":"ec3bf169f4fda2fea4e4be5ebfb9ac9f5a67d52ce3e7c11b1138dbb64930243b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:46.707085Z","signature_b64":"JgFF/7o+vIp2/N/SPL2qPz+5POHS28osZ4F4ppeSv8D9oAnyncWYzd9Rq3/2DDUcKR5snbywQl/eeNEsQjL2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11ae09664233aa88e47744a5f96584b0dd563671447fa584e45b6722f26183b1","last_reissued_at":"2026-07-05T07:20:46.706583Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:46.706583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CV","authors_text":"Jana Kierdorf, Laura Verena Junker-Frohn, Lukas Drees, Ribana Roscher","submitted_at":"2021-05-17T13:00:01Z","abstract_excerpt":"Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach's core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.07789","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2105.07789/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2105.07789","created_at":"2026-07-05T07:20:46.706644+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.07789v2","created_at":"2026-07-05T07:20:46.706644+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.07789","created_at":"2026-07-05T07:20:46.706644+00:00"},{"alias_kind":"pith_short_12","alias_value":"CGXASZSCGOVI","created_at":"2026-07-05T07:20:46.706644+00:00"},{"alias_kind":"pith_short_16","alias_value":"CGXASZSCGOVIRZDX","created_at":"2026-07-05T07:20:46.706644+00:00"},{"alias_kind":"pith_short_8","alias_value":"CGXASZSC","created_at":"2026-07-05T07:20:46.706644+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD","json":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD.json","graph_json":"https://pith.science/api/pith-number/CGXASZSCGOVIRZDXISS7SZMEWD/graph.json","events_json":"https://pith.science/api/pith-number/CGXASZSCGOVIRZDXISS7SZMEWD/events.json","paper":"https://pith.science/paper/CGXASZSC"},"agent_actions":{"view_html":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD","download_json":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD.json","view_paper":"https://pith.science/paper/CGXASZSC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.07789&json=true","fetch_graph":"https://pith.science/api/pith-number/CGXASZSCGOVIRZDXISS7SZMEWD/graph.json","fetch_events":"https://pith.science/api/pith-number/CGXASZSCGOVIRZDXISS7SZMEWD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD/action/storage_attestation","attest_author":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD/action/author_attestation","sign_citation":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD/action/citation_signature","submit_replication":"https://pith.science/pith/CGXASZSCGOVIRZDXISS7SZMEWD/action/replication_record"}},"created_at":"2026-07-05T07:20:46.706644+00:00","updated_at":"2026-07-05T07:20:46.706644+00:00"}