Carrot yield mapping: a precision agriculture approach based on machine learning (2020)
- Authors:
- USP affiliated authors: MOLIN, JOSE PAULO - ESALQ ; WEI, MARCELO CHAN FU - ESALQ ; MALDANER, LEONARDO FELIPE - ESALQ
- Unidade: ESALQ
- DOI: 10.3390/ai1020015
- Subjects: AGRICULTURA DE PRECISÃO; APRENDIZADO COMPUTACIONAL; CENOURA; IMAGEAMENTO DE SATÉLITE; MAPAS; SENSORIAMENTO REMOTO
- Language: Inglês
- Imprenta:
- Source:
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
WEI, Marcelo Chan Fu et al. Carrot yield mapping: a precision agriculture approach based on machine learning. AI, v. 1, n. 2, p. 229-241, 2020Tradução . . Disponível em: https://doi.org/10.3390/ai1020015. Acesso em: 12 fev. 2026. -
APA
Wei, M. C. F., Maldaner, L. F., Ottoni, P. M. N., & Molin, J. P. (2020). Carrot yield mapping: a precision agriculture approach based on machine learning. AI, 1( 2), 229-241. doi:10.3390/ai1020015 -
NLM
Wei MCF, Maldaner LF, Ottoni PMN, Molin JP. Carrot yield mapping: a precision agriculture approach based on machine learning [Internet]. AI. 2020 ; 1( 2): 229-241.[citado 2026 fev. 12 ] Available from: https://doi.org/10.3390/ai1020015 -
Vancouver
Wei MCF, Maldaner LF, Ottoni PMN, Molin JP. Carrot yield mapping: a precision agriculture approach based on machine learning [Internet]. AI. 2020 ; 1( 2): 229-241.[citado 2026 fev. 12 ] Available from: https://doi.org/10.3390/ai1020015 - Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
- 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
- Predictive power vs interpretability: Machine learning approaches to unravel sugarcane yield drivers
- High-resolution yield mapping for Eucalyptus grandis: a case study
- Soybean Yield Estimation and Its Components: A Linear Regression Approach
- Spatial–temporal analysis to investigate the influence of in-row plant spacing on the sugarcane yield
- Near-infrared spectroscopy as a tool for monitoring the spatial variability of sugarcane quality in the fields
- Energy analysis of sugarcane potential ethanol production from published data: a case study in Campos de Goytacazes – Brazil
- Challenges of digital solutions in sugarcane crop production: a review
Informações sobre o DOI: 10.3390/ai1020015 (Fonte: oaDOI API)
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