{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FR6DGHUEYBJ5EJZINNV67NLS77","short_pith_number":"pith:FR6DGHUE","schema_version":"1.0","canonical_sha256":"2c7c331e84c053d227286b6befb572ffd269c52081119826b1bd9e50f2265dc8","source":{"kind":"arxiv","id":"2409.12789","version":3},"attestation_state":"computed","paper":{"title":"Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Azita Dabiri, Bart De Schutter, Congcong Sun, Filippo Airaldi, Samuel Mallick","submitted_at":"2024-09-19T13:56:34Z","abstract_excerpt":"Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertaint"},"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":"2409.12789","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2024-09-19T13:56:34Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"ec47e88ec0961fbe20941c3475d9bf590717faf574fa48742428dcb7b5fff162","abstract_canon_sha256":"1329c3bf99f351ae505eed9dadfa3953d545ad09b572a00cb9fe668293b24604"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:22.132237Z","signature_b64":"x2aZ1W3m6/91robtU/uYsYgBjOeM9ItXFPUTMJjqnHBYbfoo8ERxH50dmvB6XpD3vHn3XHOLs719b+aVV5KUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c7c331e84c053d227286b6befb572ffd269c52081119826b1bd9e50f2265dc8","last_reissued_at":"2026-07-05T09:56:22.131739Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:22.131739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Azita Dabiri, Bart De Schutter, Congcong Sun, Filippo Airaldi, Samuel Mallick","submitted_at":"2024-09-19T13:56:34Z","abstract_excerpt":"Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertaint"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.12789","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/2409.12789/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":"2409.12789","created_at":"2026-07-05T09:56:22.131801+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.12789v3","created_at":"2026-07-05T09:56:22.131801+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.12789","created_at":"2026-07-05T09:56:22.131801+00:00"},{"alias_kind":"pith_short_12","alias_value":"FR6DGHUEYBJ5","created_at":"2026-07-05T09:56:22.131801+00:00"},{"alias_kind":"pith_short_16","alias_value":"FR6DGHUEYBJ5EJZI","created_at":"2026-07-05T09:56:22.131801+00:00"},{"alias_kind":"pith_short_8","alias_value":"FR6DGHUE","created_at":"2026-07-05T09:56:22.131801+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/FR6DGHUEYBJ5EJZINNV67NLS77","json":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77.json","graph_json":"https://pith.science/api/pith-number/FR6DGHUEYBJ5EJZINNV67NLS77/graph.json","events_json":"https://pith.science/api/pith-number/FR6DGHUEYBJ5EJZINNV67NLS77/events.json","paper":"https://pith.science/paper/FR6DGHUE"},"agent_actions":{"view_html":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77","download_json":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77.json","view_paper":"https://pith.science/paper/FR6DGHUE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.12789&json=true","fetch_graph":"https://pith.science/api/pith-number/FR6DGHUEYBJ5EJZINNV67NLS77/graph.json","fetch_events":"https://pith.science/api/pith-number/FR6DGHUEYBJ5EJZINNV67NLS77/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77/action/storage_attestation","attest_author":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77/action/author_attestation","sign_citation":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77/action/citation_signature","submit_replication":"https://pith.science/pith/FR6DGHUEYBJ5EJZINNV67NLS77/action/replication_record"}},"created_at":"2026-07-05T09:56:22.131801+00:00","updated_at":"2026-07-05T09:56:22.131801+00:00"}