{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V5VO73VF4JF4EPFSXBXWNCGM6Y","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"39e2c7d7ddc06b9a4e651b7ce13ef4ec530546b8f8abf383830cc3982987ce43","cross_cats_sorted":["cs.AI","cs.ET"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-03T14:03:42Z","title_canon_sha256":"61760b8dc7a2052d0b315d489ba51b24da2bea105c162b34b69c79d56452cfc7"},"schema_version":"1.0","source":{"id":"2505.01823","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.01823","created_at":"2026-07-05T11:02:24Z"},{"alias_kind":"arxiv_version","alias_value":"2505.01823v1","created_at":"2026-07-05T11:02:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.01823","created_at":"2026-07-05T11:02:24Z"},{"alias_kind":"pith_short_12","alias_value":"V5VO73VF4JF4","created_at":"2026-07-05T11:02:24Z"},{"alias_kind":"pith_short_16","alias_value":"V5VO73VF4JF4EPFS","created_at":"2026-07-05T11:02:24Z"},{"alias_kind":"pith_short_8","alias_value":"V5VO73VF","created_at":"2026-07-05T11:02:24Z"}],"graph_snapshots":[{"event_id":"sha256:5ab1ec9a2c6c7946892314cce5ac3631c1dbe080967f12a1b21787f8e48eef4e","target":"graph","created_at":"2026-07-05T11:02:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.01823/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Collecting large-scale crop disease images in the field is labor-intensive and time-consuming. Generative models (GMs) offer an alternative by creating synthetic samples that resemble real-world images. However, existing research primarily relies on Generative Adversarial Networks (GANs)-based image-to-image translation and lack a comprehensive analysis of computational requirements in agriculture. Therefore, this research explores a multi-modal text-to-image approach for generating synthetic crop disease images and is the first to provide computational benchmarking in this context. We trained","authors_text":"Arnold W. Schumann, Nathan Boyd, Nitin Rai","cross_cats":["cs.AI","cs.ET"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-03T14:03:42Z","title":"PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.01823","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b9d630612ba90704fd5e99e9bc74bb14806dc2d6073cee9d7cff53e04055d9a3","target":"record","created_at":"2026-07-05T11:02:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"39e2c7d7ddc06b9a4e651b7ce13ef4ec530546b8f8abf383830cc3982987ce43","cross_cats_sorted":["cs.AI","cs.ET"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-03T14:03:42Z","title_canon_sha256":"61760b8dc7a2052d0b315d489ba51b24da2bea105c162b34b69c79d56452cfc7"},"schema_version":"1.0","source":{"id":"2505.01823","kind":"arxiv","version":1}},"canonical_sha256":"af6aefeea5e24bc23cb2b86f6688ccf623f29180c63fec14e4d242826903eddd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af6aefeea5e24bc23cb2b86f6688ccf623f29180c63fec14e4d242826903eddd","first_computed_at":"2026-07-05T11:02:24.217900Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:02:24.217900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1pR2r8//pkRu92wyFRgqr2usONDvwyeuWfKCrKPIS0W2IXbmm3sZ7lBWlk//3TysbpmiWnx3stvwzMMNQ2wRAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:02:24.218371Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.01823","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9d630612ba90704fd5e99e9bc74bb14806dc2d6073cee9d7cff53e04055d9a3","sha256:5ab1ec9a2c6c7946892314cce5ac3631c1dbe080967f12a1b21787f8e48eef4e"],"state_sha256":"b50868477af6662cbb7625c7331f5ea8c88137410099b283e6f7c9c36ddb26bf"}