{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IUGFSVYCI5XJZIMNTTE25V6GA5","short_pith_number":"pith:IUGFSVYC","schema_version":"1.0","canonical_sha256":"450c595702476e9ca18d9cc9aed7c60740373b66316f3b33993850d22fd0fc62","source":{"kind":"arxiv","id":"2503.06608","version":2},"attestation_state":"computed","paper":{"title":"GroMo: Plant Growth Modeling with Multiview Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Abdulmotaleb El Saddik, Amanpreet Chander, Malya Singh, Mohan Kankanhalli, Mukesh Kumar Saini, Ruchi Bhatt, Rupinder Kaur, Shreya Bansal","submitted_at":"2025-03-09T13:23:16Z","abstract_excerpt":"Understanding plant growth dynamics is essential for applications in agriculture and plant phenotyping. We present the Growth Modelling (GroMo) challenge, which is designed for two primary tasks: (1) plant age prediction and (2) leaf count estimation, both essential for crop monitoring and precision agriculture. For this challenge, we introduce GroMo25, a dataset with images of four crops: radish, okra, wheat, and mustard. Each crop consists of multiple plants (p1, p2, ..., pn) captured over different days (d1, d2, ..., dm) and categorized into five levels (L1, L2, L3, L4, L5). Each plant is c"},"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":"2503.06608","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-03-09T13:23:16Z","cross_cats_sorted":["cs.LG","cs.MM"],"title_canon_sha256":"cb9ba4b1b7f6bb2e8eab64a6ef4264f842d04a09e1459eb15e2f32e2a480364d","abstract_canon_sha256":"ef009a5ca29674709355228834e0eeafb3db01bb65157336c7fe77c8da4939cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:56.481905Z","signature_b64":"V1eFaEzZ/l/VQVTF7Nk0+kwiCMwgC8n0gj44+Da01Rb0mhKbqSEaQxeXwTbbK498xxVk/42i9TNSUL92Up8QDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"450c595702476e9ca18d9cc9aed7c60740373b66316f3b33993850d22fd0fc62","last_reissued_at":"2026-07-05T11:16:56.481327Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:56.481327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GroMo: Plant Growth Modeling with Multiview Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Abdulmotaleb El Saddik, Amanpreet Chander, Malya Singh, Mohan Kankanhalli, Mukesh Kumar Saini, Ruchi Bhatt, Rupinder Kaur, Shreya Bansal","submitted_at":"2025-03-09T13:23:16Z","abstract_excerpt":"Understanding plant growth dynamics is essential for applications in agriculture and plant phenotyping. We present the Growth Modelling (GroMo) challenge, which is designed for two primary tasks: (1) plant age prediction and (2) leaf count estimation, both essential for crop monitoring and precision agriculture. For this challenge, we introduce GroMo25, a dataset with images of four crops: radish, okra, wheat, and mustard. Each crop consists of multiple plants (p1, p2, ..., pn) captured over different days (d1, d2, ..., dm) and categorized into five levels (L1, L2, L3, L4, L5). Each plant is c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06608","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/2503.06608/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":"2503.06608","created_at":"2026-07-05T11:16:56.481392+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.06608v2","created_at":"2026-07-05T11:16:56.481392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06608","created_at":"2026-07-05T11:16:56.481392+00:00"},{"alias_kind":"pith_short_12","alias_value":"IUGFSVYCI5XJ","created_at":"2026-07-05T11:16:56.481392+00:00"},{"alias_kind":"pith_short_16","alias_value":"IUGFSVYCI5XJZIMN","created_at":"2026-07-05T11:16:56.481392+00:00"},{"alias_kind":"pith_short_8","alias_value":"IUGFSVYC","created_at":"2026-07-05T11:16:56.481392+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/IUGFSVYCI5XJZIMNTTE25V6GA5","json":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5.json","graph_json":"https://pith.science/api/pith-number/IUGFSVYCI5XJZIMNTTE25V6GA5/graph.json","events_json":"https://pith.science/api/pith-number/IUGFSVYCI5XJZIMNTTE25V6GA5/events.json","paper":"https://pith.science/paper/IUGFSVYC"},"agent_actions":{"view_html":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5","download_json":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5.json","view_paper":"https://pith.science/paper/IUGFSVYC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.06608&json=true","fetch_graph":"https://pith.science/api/pith-number/IUGFSVYCI5XJZIMNTTE25V6GA5/graph.json","fetch_events":"https://pith.science/api/pith-number/IUGFSVYCI5XJZIMNTTE25V6GA5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5/action/storage_attestation","attest_author":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5/action/author_attestation","sign_citation":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5/action/citation_signature","submit_replication":"https://pith.science/pith/IUGFSVYCI5XJZIMNTTE25V6GA5/action/replication_record"}},"created_at":"2026-07-05T11:16:56.481392+00:00","updated_at":"2026-07-05T11:16:56.481392+00:00"}