{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3SDCK5YDYBL4JVCQ4AMXZPRH7X","short_pith_number":"pith:3SDCK5YD","schema_version":"1.0","canonical_sha256":"dc86257703c057c4d450e0197cbe27fdee65f538a78e0dd6bf7589c187ea93d1","source":{"kind":"arxiv","id":"2209.12557","version":1},"attestation_state":"computed","paper":{"title":"Device-friendly Guava fruit and leaf disease detection using deep learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aminul Haque Palash, Mohammed Golam Zilani, Nazmul Siddique, Rabindra Nath Nandi","submitted_at":"2022-09-26T10:19:57Z","abstract_excerpt":"This work presents a deep learning-based plant disease diagnostic system using images of fruits and leaves. Five state-of-the-art convolutional neural networks (CNN) have been employed for implementing the system. Hitherto model accuracy has been the focus for such applications and model optimization has not been accounted for the model to be applicable to end-user devices. Two model quantization techniques such as float16 and dynamic range quantization have been applied to the five state-of-the-art CNN architectures. The study shows that the quantized GoogleNet model achieved the size of 0.14"},"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":"2209.12557","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-26T10:19:57Z","cross_cats_sorted":[],"title_canon_sha256":"45d28896f9cb61d48ba8c85cb3f3cd9991ef0e2181f114c8bddcb758b60c11b4","abstract_canon_sha256":"f5ede2dfe2a46d0b0a8f3f46fcf0b575684ec9c0ff52f3d938513fa52b305d8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:00:53.715067Z","signature_b64":"iVvmjrTzY3ImqiHoMDwsoAxDG7QHr8NSIUdIlaDymXtOWQ1YBDSWSIUcl8u3g77IOn8OSutTF7AV7rPkNVGICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc86257703c057c4d450e0197cbe27fdee65f538a78e0dd6bf7589c187ea93d1","last_reissued_at":"2026-07-05T05:00:53.714703Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:00:53.714703Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Device-friendly Guava fruit and leaf disease detection using deep learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aminul Haque Palash, Mohammed Golam Zilani, Nazmul Siddique, Rabindra Nath Nandi","submitted_at":"2022-09-26T10:19:57Z","abstract_excerpt":"This work presents a deep learning-based plant disease diagnostic system using images of fruits and leaves. Five state-of-the-art convolutional neural networks (CNN) have been employed for implementing the system. Hitherto model accuracy has been the focus for such applications and model optimization has not been accounted for the model to be applicable to end-user devices. Two model quantization techniques such as float16 and dynamic range quantization have been applied to the five state-of-the-art CNN architectures. The study shows that the quantized GoogleNet model achieved the size of 0.14"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.12557","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/2209.12557/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":"2209.12557","created_at":"2026-07-05T05:00:53.714759+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.12557v1","created_at":"2026-07-05T05:00:53.714759+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.12557","created_at":"2026-07-05T05:00:53.714759+00:00"},{"alias_kind":"pith_short_12","alias_value":"3SDCK5YDYBL4","created_at":"2026-07-05T05:00:53.714759+00:00"},{"alias_kind":"pith_short_16","alias_value":"3SDCK5YDYBL4JVCQ","created_at":"2026-07-05T05:00:53.714759+00:00"},{"alias_kind":"pith_short_8","alias_value":"3SDCK5YD","created_at":"2026-07-05T05:00:53.714759+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/3SDCK5YDYBL4JVCQ4AMXZPRH7X","json":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X.json","graph_json":"https://pith.science/api/pith-number/3SDCK5YDYBL4JVCQ4AMXZPRH7X/graph.json","events_json":"https://pith.science/api/pith-number/3SDCK5YDYBL4JVCQ4AMXZPRH7X/events.json","paper":"https://pith.science/paper/3SDCK5YD"},"agent_actions":{"view_html":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X","download_json":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X.json","view_paper":"https://pith.science/paper/3SDCK5YD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.12557&json=true","fetch_graph":"https://pith.science/api/pith-number/3SDCK5YDYBL4JVCQ4AMXZPRH7X/graph.json","fetch_events":"https://pith.science/api/pith-number/3SDCK5YDYBL4JVCQ4AMXZPRH7X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X/action/storage_attestation","attest_author":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X/action/author_attestation","sign_citation":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X/action/citation_signature","submit_replication":"https://pith.science/pith/3SDCK5YDYBL4JVCQ4AMXZPRH7X/action/replication_record"}},"created_at":"2026-07-05T05:00:53.714759+00:00","updated_at":"2026-07-05T05:00:53.714759+00:00"}