Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices (2025)
- Authors:
- USP affiliated authors: FIORIO, PETERSON RICARDO - ESALQ ; ALEXANDRE, MARTA LAURA DE SOUZA - ESALQ ; LIMA, IZABELLE DE LIMA E - ESALQ ; NILSSON, MATHEUS STERZO - ESALQ ; RIZZO, RODNEI - ESALQ ; SILVA, CARLOS AUGUSTO ALVES CARDOSO - ESALQ
- Unidade: ESALQ
- DOI: 10.3390/agronomy15092149
- Subjects: ANÁLISE ESPECTRAL; APRENDIZADO COMPUTACIONAL; BIOMETRIA; CANA-DE-AÇÚCAR; SENSOR
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
ALEXANDRE, Marta Laura de Souza et al. Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices. Agronomy, v. 15, p. 1-20, 2025Tradução . . Disponível em: https://doi.org/10.3390/agronomy15092149. Acesso em: 12 fev. 2026. -
APA
Alexandre, M. L. de S., Lima, I. de L. e, Nilsson, M. S., Rizzo, R., Silva, C. A. A. C., & Fiorio, P. R. (2025). Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices. Agronomy, 15, 1-20. doi:10.3390/agronomy15092149 -
NLM
Alexandre ML de S, Lima I de L e, Nilsson MS, Rizzo R, Silva CAAC, Fiorio PR. Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices [Internet]. Agronomy. 2025 ; 15 1-20.[citado 2026 fev. 12 ] Available from: https://doi.org/10.3390/agronomy15092149 -
Vancouver
Alexandre ML de S, Lima I de L e, Nilsson MS, Rizzo R, Silva CAAC, Fiorio PR. Sugarcane (Saccharum officinarum) productivity estimation using multispectral sensors in RPAs, biometric variables, and vegetation indices [Internet]. Agronomy. 2025 ; 15 1-20.[citado 2026 fev. 12 ] Available from: https://doi.org/10.3390/agronomy15092149 - Interspecies prediction of nitrogen content in processed plant samples using spectroscopic modeling and transfer learning
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Informações sobre o DOI: 10.3390/agronomy15092149 (Fonte: oaDOI API)
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