{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:K5YP4BMNL5QMHYCYKNMPMJRIRS","short_pith_number":"pith:K5YP4BMN","schema_version":"1.0","canonical_sha256":"5770fe058d5f60c3e0585358f626288ca9bca25b720a8fead395437048bfca41","source":{"kind":"arxiv","id":"2601.02353","version":3},"attestation_state":"computed","paper":{"title":"Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Afroze Begum, Dr Fahmina Taranum, Dr Tasneem Bano Rehman, Mohammed Kaif Pasha, Mohammed Mudassir Uddin, Shahnawaz Alam","submitted_at":"2026-01-05T18:55:05Z","abstract_excerpt":"Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performance computing resources. Deep learning models can detect diseases from leaf images with high accuracy, but these models are typically too large and computationally expensive to run on low-cost edge devices such as Raspberry Pi. Furthermore, collecting thousands of labeled disease images for training is both expensive and time-consuming. This paper addresses both challenges by combining neural network pruning, removing unnecessary parts of the model,"},"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":"2601.02353","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-05T18:55:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dfc308a89df46cb71a4f1c55977560a3600e53b5b9f3796c01dcf5cfc3fdf087","abstract_canon_sha256":"279ca3c32e43377eb10ac713e3072f2e9f385d8fed2a7ba1dc5c8aff0dffbc8f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:01.641701Z","signature_b64":"IdS6uROnv9Ttg1O3VMN8qLs4z3HJ00At2qd80++9H59g/x9KZ9iZ+b/1r+vcHxRNthO0/0DT3e5E3/XNmQ67DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5770fe058d5f60c3e0585358f626288ca9bca25b720a8fead395437048bfca41","last_reissued_at":"2026-05-20T00:03:01.641000Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:01.641000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Afroze Begum, Dr Fahmina Taranum, Dr Tasneem Bano Rehman, Mohammed Kaif Pasha, Mohammed Mudassir Uddin, Shahnawaz Alam","submitted_at":"2026-01-05T18:55:05Z","abstract_excerpt":"Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performance computing resources. Deep learning models can detect diseases from leaf images with high accuracy, but these models are typically too large and computationally expensive to run on low-cost edge devices such as Raspberry Pi. Furthermore, collecting thousands of labeled disease images for training is both expensive and time-consuming. This paper addresses both challenges by combining neural network pruning, removing unnecessary parts of the model,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.02353","kind":"arxiv","version":3},"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/2601.02353/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":"2601.02353","created_at":"2026-05-20T00:03:01.641109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.02353v3","created_at":"2026-05-20T00:03:01.641109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02353","created_at":"2026-05-20T00:03:01.641109+00:00"},{"alias_kind":"pith_short_12","alias_value":"K5YP4BMNL5QM","created_at":"2026-05-20T00:03:01.641109+00:00"},{"alias_kind":"pith_short_16","alias_value":"K5YP4BMNL5QMHYCY","created_at":"2026-05-20T00:03:01.641109+00:00"},{"alias_kind":"pith_short_8","alias_value":"K5YP4BMN","created_at":"2026-05-20T00:03:01.641109+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/K5YP4BMNL5QMHYCYKNMPMJRIRS","json":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS.json","graph_json":"https://pith.science/api/pith-number/K5YP4BMNL5QMHYCYKNMPMJRIRS/graph.json","events_json":"https://pith.science/api/pith-number/K5YP4BMNL5QMHYCYKNMPMJRIRS/events.json","paper":"https://pith.science/paper/K5YP4BMN"},"agent_actions":{"view_html":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS","download_json":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS.json","view_paper":"https://pith.science/paper/K5YP4BMN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.02353&json=true","fetch_graph":"https://pith.science/api/pith-number/K5YP4BMNL5QMHYCYKNMPMJRIRS/graph.json","fetch_events":"https://pith.science/api/pith-number/K5YP4BMNL5QMHYCYKNMPMJRIRS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS/action/storage_attestation","attest_author":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS/action/author_attestation","sign_citation":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS/action/citation_signature","submit_replication":"https://pith.science/pith/K5YP4BMNL5QMHYCYKNMPMJRIRS/action/replication_record"}},"created_at":"2026-05-20T00:03:01.641109+00:00","updated_at":"2026-05-20T00:03:01.641109+00:00"}