Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy (2021)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; CORRÊDO, LUCAS DE PAULA - ESALQ ; MALDANER, LEONARDO FELIPE - ESALQ ; BAZAME, HELIZANI COUTO - ESALQ
- Unidade: ESALQ
- DOI: 10.3390/s21062195
- Subjects: AGRICULTURA DE PRECISÃO; AMOSTRAGEM; CANA-DE-AÇÚCAR; ESPECTROSCOPIA INFRAVERMELHA; QUIMIOMETRIA; SENSOR
- Keywords: Detecção proximal
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
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ABNT
CORRÊDO, Lucas de Paula et al. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors, v. 21, p. 1-23, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21062195. Acesso em: 01 abr. 2026. -
APA
Corrêdo, L. de P., Maldaner, L. F., Bazame, H. C., & Molin, J. P. (2021). Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors, 21, 1-23. doi:10.3390/s21062195 -
NLM
Corrêdo L de P, Maldaner LF, Bazame HC, Molin JP. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy [Internet]. Sensors. 2021 ; 21 1-23.[citado 2026 abr. 01 ] Available from: https://doi.org/10.3390/s21062195 -
Vancouver
Corrêdo L de P, Maldaner LF, Bazame HC, Molin JP. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy [Internet]. Sensors. 2021 ; 21 1-23.[citado 2026 abr. 01 ] Available from: https://doi.org/10.3390/s21062195 - Definition of optimal maize seeding rates based on the potential yield of management zones
- Precision agriculture and the digital contributions for site-specific management of the fields
- Mapping coffee yield with computer vision
- Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches
- Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester
- Methodology to filter out outliers in high spatial density data to improve maps reliability
- Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives
- Use of active sensors in coffee cultivation for monitoring crop yield
- Near-infrared spectroscopy as a tool for monitoring the spatial variability of sugarcane quality in the fields
- Spatial variability mapping of sugarcane qualitative attributes
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