pith:XCDNLU2R
PesTwin: a biology-informed Digital Twin for enabling precision farming
A digital twin uses agent-based rules to forecast pest infestations on crops from lab, weather, and field data.
arxiv:2603.12294 v2 · 2026-03-11 · q-bio.QM · cs.SE · q-bio.PE
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Claims
Through a flexible rule-based approach of the Agent-Based Modeling (ABM) paradigm, the framework supports the fine-tuning of the main ecological interactions of the pest with its crop host and the environment. Forecasting insect infestation in realistic scenarios, considering both spatial and temporal dimensions, is made possible by integrating heterogeneous data sources.
That the rule-based agent-based model, when tuned with the integrated lab, weather, and GIS data, will produce forecasts that accurately reflect real-world pest dynamics and ecological interactions for the target species.
PesTwin is a biology-informed digital twin that employs flexible rule-based agent-based modeling to simulate and forecast pest infestations by integrating laboratory, environmental, and spatial data, demonstrated on Drosophila suzukii.
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Receipt and verification
| First computed | 2026-05-17T23:39:04.378886Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b886d5d351253e2f488a09fe9690f92fa54827983fb62adb0ca179a6a07cadf6
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/XCDNLU2REU7C6SEKBH7JNEHZF6 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b886d5d351253e2f488a09fe9690f92fa54827983fb62adb0ca179a6a07cadf6
Canonical record JSON
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