A first step towards data ecosystem design for AI in agriculture: a case study assessing variable importance for optimising nitrogen decision-making in wheat (2025)
- Authors:
- Autor USP: COLACO, ANDRE FREITAS - ESALQ
- Unidade: ESALQ
- DOI: 10.1016/j.atech.2025.101431
- Subjects: AGRICULTURA DE PRECISÃO; APRENDIZADO COMPUTACIONAL; FERTILIZANTES NITROGENADOS; INTELIGÊNCIA ARTIFICIAL; NITROGÊNIO; TOMADA DE DECISÃO; TRIGO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Smart Agricultural Technology
- ISSN: 2772-3755
- Volume/Número/Paginação/Ano: v. 12, art. 101431, p. 1-10, 2025
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
RICHETTI, Jonathan e COLAÇO, Andre Freitas e LAWES, Roger A. A first step towards data ecosystem design for AI in agriculture: a case study assessing variable importance for optimising nitrogen decision-making in wheat. Smart Agricultural Technology, v. 12, p. 1-10, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.atech.2025.101431. Acesso em: 21 jan. 2026. -
APA
Richetti, J., Colaço, A. F., & Lawes, R. A. (2025). A first step towards data ecosystem design for AI in agriculture: a case study assessing variable importance for optimising nitrogen decision-making in wheat. Smart Agricultural Technology, 12, 1-10. doi:10.1016/j.atech.2025.101431 -
NLM
Richetti J, Colaço AF, Lawes RA. A first step towards data ecosystem design for AI in agriculture: a case study assessing variable importance for optimising nitrogen decision-making in wheat [Internet]. Smart Agricultural Technology. 2025 ; 12 1-10.[citado 2026 jan. 21 ] Available from: https://doi.org/10.1016/j.atech.2025.101431 -
Vancouver
Richetti J, Colaço AF, Lawes RA. A first step towards data ecosystem design for AI in agriculture: a case study assessing variable importance for optimising nitrogen decision-making in wheat [Internet]. Smart Agricultural Technology. 2025 ; 12 1-10.[citado 2026 jan. 21 ] Available from: https://doi.org/10.1016/j.atech.2025.101431 - What makes on-farm experimental data suitable for data-driven decision-making?: implications of trial design and spatial distribution of field data for machine learning models
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Informações sobre o DOI: 10.1016/j.atech.2025.101431 (Fonte: oaDOI API)
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