Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester (2021)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; MALDANER, LEONARDO FELIPE - ESALQ
- Unidade: ESALQ
- DOI: 10.3390/s21134530
- Subjects: AGRICULTURA DE PRECISÃO; CANA-DE-AÇÚCAR; COLHEDORAS; REDES NEURAIS; SENSOR
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Sensors
- Volume/Número/Paginação/Ano: v. 21, art. 4530, p. 1-14, 2021
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
LIMA, Jeovano de Jesus Alves de e MALDANER, Leonardo Felipe e MOLIN, José Paulo. Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester. Sensors, v. 21, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21134530. Acesso em: 26 abr. 2024. -
APA
Lima, J. de J. A. de, Maldaner, L. F., & Molin, J. P. (2021). Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester. Sensors, 21, 1-14. doi:10.3390/s21134530 -
NLM
Lima J de JA de, Maldaner LF, Molin JP. Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester [Internet]. Sensors. 2021 ; 21 1-14.[citado 2024 abr. 26 ] Available from: https://doi.org/10.3390/s21134530 -
Vancouver
Lima J de JA de, Maldaner LF, Molin JP. Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester [Internet]. Sensors. 2021 ; 21 1-14.[citado 2024 abr. 26 ] Available from: https://doi.org/10.3390/s21134530 - Methodology to filter out outliers in high spatial density data to improve maps reliability
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- Processamento de dados de monitores de produtividade de cana-de-açúcar
- Sugarcane plant detection and mapping for site-specific management
- Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
- Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches
- 3D data processing to characterize the spatial variability of sugarcane fields
- A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations
- A system for plant detection using sensor fusion approach based on machine learning model
Informações sobre o DOI: 10.3390/s21134530 (Fonte: oaDOI API)
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