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Context-Aware Adaptive Pesticide Spraying for Agricultural Robots under Changing Weather and Terrain Using Vision-Language Models — arXiv:2610.08807

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.08807
License (as archived): CC0-1.0
Archived: 2026-10-08T12:48:45.305Z
SHA-256 of the archived text: d955670b5ea15b700be3ee719da0c808be8c2eff7e254a1dabca7e5cb4c08041
Stable URL of this page: https://metraipolis.com/documento/dc_022e0d02bdfd

How to cite

Context-Aware Adaptive Pesticide Spraying for Agricultural Robots under Changing Weather and Terrain Using Vision-Language Models — arXiv:2610.08807 — arXiv (en), https://arxiv.org/abs/2610.08807 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_022e0d02bdfd (Metraipolis archive dc_022e0d02bdfd, sha256 d955670b5ea15b700be3ee719da0c808be8c2eff7e254a1dabca7e5cb4c08041)

BibTeX

@misc{dc022e0d02bdfd,
  title = {Context-Aware Adaptive Pesticide Spraying for Agricultural Robots under Changing Weather and Terrain Using Vision-Language Models — arXiv:2610.08807},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.08807},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_022e0d02bdfd; sha256 d955670b5ea15b700be3ee719da0c808be8c2eff7e254a1dabca7e5cb4c08041},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.08807. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Precision pesticide spraying is essential for optimizing application efficiency and ensuring uniform chemical distribution. Spraying performance is influenced by multiple factors, including environmental conditions such as temperature and wind speed, pesticide type, and the robot's capability to accurately perceive crops and target spray locations. Existing approaches predominantly emphasize crop detection and rely on predefined spraying parameters, whereas human operators dynamically adjust their spraying strategies by considering environmental conditions, region-specific crop characteristics, and the type of pesticide being applied. In this study, we propose a context-aware adaptive spraying framework based on Vision-Language Models (VLMs), which enables robots to leverage spatial reasoning and integrate information from multiple sources, including crop type, pesticide type, and weather data, to make adaptive and optimized spraying decisions. Subsequently, a trajectory-tracking controller based on Model Predictive Path Integral (MPPI) control is employed to ensure precise navigation and accurate spraying at crop locations. The comparative results demonstrate that the proposed method improves accuracy by at least 30% in detecting crop rows. In addition, the experimental evaluations conducted in two environments further demonstrate the robot's ability to flexibly adjust spraying volume and travel speed, while reducing pesticide drift.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.08807 — 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 2026-10-08T12:18:30.475Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).