Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches (2021)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; MALDANER, LEONARDO FELIPE - ESALQ ; CORRÊDO, LUCAS DE PAULA - ESALQ ; CANATA, TATIANA FERNANDA - ESALQ
- Unidade: ESALQ
- DOI: 10.1016/j.compag.2020.105945
- Subjects: AGRICULTURA DE PRECISÃO; COMPUTACIONAL; CANA-DE-AÇÚCAR; COLHEDORAS
- Keywords: Monitor de rendimento
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Computers and Electronics in Agriculture
- ISSN: 1872-7107
- Volume/Número/Paginação/Ano: v. 181, art. 105945, p. 1-9, 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
MALDANER, Leonardo Felipe et al. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches. Computers and Electronics in Agriculture, v. 181, p. 1-9, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105945. Acesso em: 23 abr. 2024. -
APA
Maldaner, L. F., Corrêdo, L. de P., Canata, T. F., & Molin, J. P. (2021). Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches. Computers and Electronics in Agriculture, 181, 1-9. doi:10.1016/j.compag.2020.105945 -
NLM
Maldaner LF, Corrêdo L de P, Canata TF, Molin JP. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches [Internet]. Computers and Electronics in Agriculture. 2021 ; 181 1-9.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1016/j.compag.2020.105945 -
Vancouver
Maldaner LF, Corrêdo L de P, Canata TF, Molin JP. Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches [Internet]. Computers and Electronics in Agriculture. 2021 ; 181 1-9.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1016/j.compag.2020.105945 - Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives
- Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
- Precision agriculture and the digital contributions for site-specific management of the fields
- A system for plant detection using sensor fusion approach based on machine learning model
- Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy
- High-resolution imagery data to assess the spatial variability of sugarcane fields
- 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
- Methodology to filter out outliers in high spatial density data to improve maps reliability
- Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester
Informações sobre o DOI: 10.1016/j.compag.2020.105945 (Fonte: oaDOI API)
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