Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives (2020)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; CORRÊDO, LUCAS DE PAULA - ESALQ ; CANATA, TATIANA FERNANDA - ESALQ ; MALDANER, LEONARDO FELIPE - ESALQ ; LIMA, JEOVANO DE JESUS ALVES DE - ESALQ
- Unidade: ESALQ
- DOI: 10.1007/s12355-020-00874-3
- Subjects: AGRICULTURA DE PRECISÃO; CANA-DE-AÇÚCAR; COLHEDORAS; SENSOR; VARIABILIDADE ESPACIAL
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2020
- Source:
- Título do periódico: Sugar Tech
- ISSN: 0972-1525
- Volume/Número/Paginação/Ano: online, p. 1-14, 2020
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CORRÊDO, Lucas de Paula et al. Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives. Sugar Tech, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1007/s12355-020-00874-3. Acesso em: 07 maio 2024. -
APA
Corrêdo, L. de P., Canata, T. F., Maldaner, L. F., Lima, J. de J. A. de, & Molin, J. P. (2020). Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives. Sugar Tech, 1-14. doi:10.1007/s12355-020-00874-3 -
NLM
Corrêdo L de P, Canata TF, Maldaner LF, Lima J de JA de, Molin JP. Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives [Internet]. Sugar Tech. 2020 ; 1-14.[citado 2024 maio 07 ] Available from: https://doi.org/10.1007/s12355-020-00874-3 -
Vancouver
Corrêdo L de P, Canata TF, Maldaner LF, Lima J de JA de, Molin JP. Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives [Internet]. Sugar Tech. 2020 ; 1-14.[citado 2024 maio 07 ] Available from: https://doi.org/10.1007/s12355-020-00874-3 - Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches
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- A system for plant detection using sensor fusion approach based on machine learning model
- 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: 10.1007/s12355-020-00874-3 (Fonte: oaDOI API)
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