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Utility-Driven Spatial Data Sampling for UAV-Assisted Scientific Smart Farming — arXiv:2609.05765

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05765
License (as archived): CC0-1.0
Archived: 2026-10-16T13:29:58.297Z
SHA-256 of the archived text: 545a03041f0047d3dd2cc89a36edb8403a8f079d98080b9e0fe791657333ce27
Stable URL of this page: https://metraipolis.com/documento/dc_edc8eb32fd00

How to cite

Utility-Driven Spatial Data Sampling for UAV-Assisted Scientific Smart Farming — arXiv:2609.05765 — arXiv (en), https://arxiv.org/abs/2609.05765 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_edc8eb32fd00 (Metraipolis archive dc_edc8eb32fd00, sha256 545a03041f0047d3dd2cc89a36edb8403a8f079d98080b9e0fe791657333ce27)

BibTeX

@misc{dcedc8eb32fd00,
  title = {Utility-Driven Spatial Data Sampling for UAV-Assisted Scientific Smart Farming — arXiv:2609.05765},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05765},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_edc8eb32fd00; sha256 545a03041f0047d3dd2cc89a36edb8403a8f079d98080b9e0fe791657333ce27},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05765. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large smart-farming deployments generate continuous scientific data from spatially distributed sensors, including soil, humidity, temperature, crop-health, and pest-related measurements. In vast agricultural fields, however, an energy-constrained unmanned aerial vehicle (UAV) often cannot collect data from every sensor during each mission. Existing UAV-assisted collection methods typically optimize coverage, route length, data volume, or freshness, but they do not always distinguish between data that is merely available and data that is scientifically valuable. This poster introduces a utility-driven spatial sampling framework for UAV-assisted smart farming. The field is partitioned into grid cells, each sized according to the UAV ground coverage range. After an initial exploration phase, each cell receives a scientific utility score based on freshness, redundancy, anomaly likelihood, and model uncertainty. The UAV then selects and visits a subset of high-utility cells under battery and return-to-base constraints. The proposed framework reframes UAV-based collection as adaptive scientific data management rather than exhaustive sensing.

Tema degli esploratori: economia dei servizi IA (retro-catalogo arXiv: annunci del 2026-09-09 in cs.GT).
Categorie arXiv: Computer Science and Game Theory (cs.GT).
Pagina del lavoro: https://arxiv.org/abs/2609.05765 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:06:09.160Z dalla pagina https://arxiv.org/catchup/cs.GT/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).