{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:I3YJAF6DVPYFZDI33XBOSPG6Y7","short_pith_number":"pith:I3YJAF6D","schema_version":"1.0","canonical_sha256":"46f09017c3abf05c8d1bddc2e93cdec7fc266f5004f27e34b635a8b9d6dc7aac","source":{"kind":"arxiv","id":"1707.03717","version":2},"attestation_state":"computed","paper":{"title":"Using Transfer Learning for Image-Based Cassava Disease Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.CV","authors_text":"Amanda Ramcharan, Babuali Ahmed, David Hughes, James Legg, Kelsee Baranowski, Peter McCloskey","submitted_at":"2017-06-19T15:01:59Z","abstract_excerpt":"Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New transfer learning methods offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learni"},"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":"1707.03717","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-19T15:01:59Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"19b98ca0a364a7405165900862b4a247118deccb70b726fb843b8981941ed4ff","abstract_canon_sha256":"26b2a15f06d43a89aa22ae685db78db1963a25eb56f9ba87b7261c55e110e95e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:19.135127Z","signature_b64":"oJzzMl/fecysw0rCLDCFvsQRC6Qqh/xP8vwXJ0wvIfua/j7r+k6JZhAHTLo2Kr2Umcwcu+il/4+eWnZWcMApCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46f09017c3abf05c8d1bddc2e93cdec7fc266f5004f27e34b635a8b9d6dc7aac","last_reissued_at":"2026-05-18T00:31:19.134403Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:19.134403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Transfer Learning for Image-Based Cassava Disease Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.CV","authors_text":"Amanda Ramcharan, Babuali Ahmed, David Hughes, James Legg, Kelsee Baranowski, Peter McCloskey","submitted_at":"2017-06-19T15:01:59Z","abstract_excerpt":"Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New transfer learning methods offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.03717","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":""},"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":"1707.03717","created_at":"2026-05-18T00:31:19.134544+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.03717v2","created_at":"2026-05-18T00:31:19.134544+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.03717","created_at":"2026-05-18T00:31:19.134544+00:00"},{"alias_kind":"pith_short_12","alias_value":"I3YJAF6DVPYF","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"I3YJAF6DVPYFZDI3","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"I3YJAF6D","created_at":"2026-05-18T12:31:21.493067+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/I3YJAF6DVPYFZDI33XBOSPG6Y7","json":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7.json","graph_json":"https://pith.science/api/pith-number/I3YJAF6DVPYFZDI33XBOSPG6Y7/graph.json","events_json":"https://pith.science/api/pith-number/I3YJAF6DVPYFZDI33XBOSPG6Y7/events.json","paper":"https://pith.science/paper/I3YJAF6D"},"agent_actions":{"view_html":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7","download_json":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7.json","view_paper":"https://pith.science/paper/I3YJAF6D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.03717&json=true","fetch_graph":"https://pith.science/api/pith-number/I3YJAF6DVPYFZDI33XBOSPG6Y7/graph.json","fetch_events":"https://pith.science/api/pith-number/I3YJAF6DVPYFZDI33XBOSPG6Y7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7/action/storage_attestation","attest_author":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7/action/author_attestation","sign_citation":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7/action/citation_signature","submit_replication":"https://pith.science/pith/I3YJAF6DVPYFZDI33XBOSPG6Y7/action/replication_record"}},"created_at":"2026-05-18T00:31:19.134544+00:00","updated_at":"2026-05-18T00:31:19.134544+00:00"}