{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BDIINE4KFGOJFTACGJ3UXH6PIY","short_pith_number":"pith:BDIINE4K","schema_version":"1.0","canonical_sha256":"08d086938a299c92cc0232774b9fcf4613df40aa8c206ddf52c61d3cc3f000d9","source":{"kind":"arxiv","id":"2408.00749","version":1},"attestation_state":"computed","paper":{"title":"Leaf Angle Estimation using Mask R-CNN and LETR Vision Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Prapti Thapaliya, Trevor Rife, Venkat Margapuri","submitted_at":"2024-08-01T17:52:10Z","abstract_excerpt":"Modern day studies show a high degree of correlation between high yielding crop varieties and plants with upright leaf angles. It is observed that plants with upright leaf angles intercept more light than those without upright leaf angles, leading to a higher rate of photosynthesis. Plant scientists and breeders benefit from tools that can directly measure plant parameters in the field i.e. on-site phenotyping. The estimation of leaf angles by manual means in a field setting is tedious and cumbersome. We mitigate the tedium using a combination of the Mask R-CNN instance segmentation neural net"},"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":"2408.00749","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-01T17:52:10Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ea43a6217108d9fc080383345aac43d3ca7780cbf3e522f3fed5093b912545bd","abstract_canon_sha256":"a007e6b16c280677399c8d9d49d6ae47850c82afbb42642b864a9f76d214f5ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:08.207589Z","signature_b64":"YvmaMAFYdRSiNRTyhT+CE7hlKwu1/mHLxuNrnsMaSOkEWEIeM4/Yoh4PCWR+X8u13sCMS5qTzm9XcOP7LvepAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08d086938a299c92cc0232774b9fcf4613df40aa8c206ddf52c61d3cc3f000d9","last_reissued_at":"2026-07-05T08:51:08.207180Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:08.207180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leaf Angle Estimation using Mask R-CNN and LETR Vision Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Prapti Thapaliya, Trevor Rife, Venkat Margapuri","submitted_at":"2024-08-01T17:52:10Z","abstract_excerpt":"Modern day studies show a high degree of correlation between high yielding crop varieties and plants with upright leaf angles. It is observed that plants with upright leaf angles intercept more light than those without upright leaf angles, leading to a higher rate of photosynthesis. Plant scientists and breeders benefit from tools that can directly measure plant parameters in the field i.e. on-site phenotyping. The estimation of leaf angles by manual means in a field setting is tedious and cumbersome. We mitigate the tedium using a combination of the Mask R-CNN instance segmentation neural net"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.00749","kind":"arxiv","version":1},"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/2408.00749/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":"2408.00749","created_at":"2026-07-05T08:51:08.207234+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.00749v1","created_at":"2026-07-05T08:51:08.207234+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.00749","created_at":"2026-07-05T08:51:08.207234+00:00"},{"alias_kind":"pith_short_12","alias_value":"BDIINE4KFGOJ","created_at":"2026-07-05T08:51:08.207234+00:00"},{"alias_kind":"pith_short_16","alias_value":"BDIINE4KFGOJFTAC","created_at":"2026-07-05T08:51:08.207234+00:00"},{"alias_kind":"pith_short_8","alias_value":"BDIINE4K","created_at":"2026-07-05T08:51:08.207234+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/BDIINE4KFGOJFTACGJ3UXH6PIY","json":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY.json","graph_json":"https://pith.science/api/pith-number/BDIINE4KFGOJFTACGJ3UXH6PIY/graph.json","events_json":"https://pith.science/api/pith-number/BDIINE4KFGOJFTACGJ3UXH6PIY/events.json","paper":"https://pith.science/paper/BDIINE4K"},"agent_actions":{"view_html":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY","download_json":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY.json","view_paper":"https://pith.science/paper/BDIINE4K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.00749&json=true","fetch_graph":"https://pith.science/api/pith-number/BDIINE4KFGOJFTACGJ3UXH6PIY/graph.json","fetch_events":"https://pith.science/api/pith-number/BDIINE4KFGOJFTACGJ3UXH6PIY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY/action/storage_attestation","attest_author":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY/action/author_attestation","sign_citation":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY/action/citation_signature","submit_replication":"https://pith.science/pith/BDIINE4KFGOJFTACGJ3UXH6PIY/action/replication_record"}},"created_at":"2026-07-05T08:51:08.207234+00:00","updated_at":"2026-07-05T08:51:08.207234+00:00"}