{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V5VO73VF4JF4EPFSXBXWNCGM6Y","short_pith_number":"pith:V5VO73VF","schema_version":"1.0","canonical_sha256":"af6aefeea5e24bc23cb2b86f6688ccf623f29180c63fec14e4d242826903eddd","source":{"kind":"arxiv","id":"2505.01823","version":1},"attestation_state":"computed","paper":{"title":"PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET"],"primary_cat":"cs.CV","authors_text":"Arnold W. Schumann, Nathan Boyd, Nitin Rai","submitted_at":"2025-05-03T14:03:42Z","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"},"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":"2505.01823","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-03T14:03:42Z","cross_cats_sorted":["cs.AI","cs.ET"],"title_canon_sha256":"61760b8dc7a2052d0b315d489ba51b24da2bea105c162b34b69c79d56452cfc7","abstract_canon_sha256":"39e2c7d7ddc06b9a4e651b7ce13ef4ec530546b8f8abf383830cc3982987ce43"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:24.218371Z","signature_b64":"1pR2r8//pkRu92wyFRgqr2usONDvwyeuWfKCrKPIS0W2IXbmm3sZ7lBWlk//3TysbpmiWnx3stvwzMMNQ2wRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af6aefeea5e24bc23cb2b86f6688ccf623f29180c63fec14e4d242826903eddd","last_reissued_at":"2026-07-05T11:02:24.217900Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:24.217900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET"],"primary_cat":"cs.CV","authors_text":"Arnold W. Schumann, Nathan Boyd, Nitin Rai","submitted_at":"2025-05-03T14:03:42Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.01823","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/2505.01823/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":"2505.01823","created_at":"2026-07-05T11:02:24.217954+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.01823v1","created_at":"2026-07-05T11:02:24.217954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.01823","created_at":"2026-07-05T11:02:24.217954+00:00"},{"alias_kind":"pith_short_12","alias_value":"V5VO73VF4JF4","created_at":"2026-07-05T11:02:24.217954+00:00"},{"alias_kind":"pith_short_16","alias_value":"V5VO73VF4JF4EPFS","created_at":"2026-07-05T11:02:24.217954+00:00"},{"alias_kind":"pith_short_8","alias_value":"V5VO73VF","created_at":"2026-07-05T11:02:24.217954+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/V5VO73VF4JF4EPFSXBXWNCGM6Y","json":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y.json","graph_json":"https://pith.science/api/pith-number/V5VO73VF4JF4EPFSXBXWNCGM6Y/graph.json","events_json":"https://pith.science/api/pith-number/V5VO73VF4JF4EPFSXBXWNCGM6Y/events.json","paper":"https://pith.science/paper/V5VO73VF"},"agent_actions":{"view_html":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y","download_json":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y.json","view_paper":"https://pith.science/paper/V5VO73VF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.01823&json=true","fetch_graph":"https://pith.science/api/pith-number/V5VO73VF4JF4EPFSXBXWNCGM6Y/graph.json","fetch_events":"https://pith.science/api/pith-number/V5VO73VF4JF4EPFSXBXWNCGM6Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y/action/storage_attestation","attest_author":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y/action/author_attestation","sign_citation":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y/action/citation_signature","submit_replication":"https://pith.science/pith/V5VO73VF4JF4EPFSXBXWNCGM6Y/action/replication_record"}},"created_at":"2026-07-05T11:02:24.217954+00:00","updated_at":"2026-07-05T11:02:24.217954+00:00"}