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Artigo de periodico
When ratoon longevity matters: Spatial and temporal analysis of sugarcane plant population patterns using features extracted from UAV images (2025)
Maldaner, Leonardo Felipe ; Molin, José Paulo ; Dias, Carlos Tadeu dos Santos ; Silva, Eudocio Rafael Otavio da
Source: Remote Sensing Applications: Society and Environment. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, DINÂMICA TEMPORAL, GEOESTATÍSTICA, POPULAÇÕES VEGETAIS, PROCESSAMENTO DE IMAGENS, VARIABILIDADE ESPACIAL
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ABNT
MALDANER, Leonardo Felipe et al. When ratoon longevity matters: Spatial and temporal analysis of sugarcane plant population patterns using features extracted from UAV images. Remote Sensing Applications: Society and Environment, v. 39, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.rsase.2025.101703. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., Molin, J. P., Dias, C. T. dos S., & Silva, E. R. O. da. (2025). When ratoon longevity matters: Spatial and temporal analysis of sugarcane plant population patterns using features extracted from UAV images. Remote Sensing Applications: Society and Environment, 39. doi:10.1016/j.rsase.2025.101703
NLM
Maldaner LF, Molin JP, Dias CT dos S, Silva ERO da. When ratoon longevity matters: Spatial and temporal analysis of sugarcane plant population patterns using features extracted from UAV images [Internet]. Remote Sensing Applications: Society and Environment. 2025 ; 39[citado 2025 out. 09 ] Available from: https://doi.org/10.1016/j.rsase.2025.101703
Vancouver
Maldaner LF, Molin JP, Dias CT dos S, Silva ERO da. When ratoon longevity matters: Spatial and temporal analysis of sugarcane plant population patterns using features extracted from UAV images [Internet]. Remote Sensing Applications: Society and Environment. 2025 ; 39[citado 2025 out. 09 ] Available from: https://doi.org/10.1016/j.rsase.2025.101703
Artigo de periodico
Spatial–temporal analysis to investigate the influence of in-row plant spacing on the sugarcane yield (2024)
Maldaner, Leonardo Felipe ; Molin, José Paulo ; Silva, Eudocio Rafael Otavio da
Source: Sugar Tech. Unidade: ESALQ
Subjects: AEROFOTOGRAMETRIA, AERONAVES NÃO TRIPULADAS, AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, DENSIDADE POPULACIONAL, ESPAÇAMENTO, VARIABILIDADE ESPACIAL
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ABNT
MALDANER, Leonardo Felipe e MOLIN, José Paulo e SILVA, Eudocio Rafael Otavio da. Spatial–temporal analysis to investigate the influence of in-row plant spacing on the sugarcane yield. Sugar Tech, v. 26, n. 1, p. 194–206, 2024Tradução . . Disponível em: https://doi.org/10.1007/s12355-023-01334-4. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., Molin, J. P., & Silva, E. R. O. da. (2024). Spatial–temporal analysis to investigate the influence of in-row plant spacing on the sugarcane yield. Sugar Tech, 26( 1), 194–206. doi:10.1007/s12355-023-01334-4
NLM
Maldaner LF, Molin JP, Silva ERO da. Spatial–temporal analysis to investigate the influence of in-row plant spacing on the sugarcane yield [Internet]. Sugar Tech. 2024 ; 26( 1): 194–206.[citado 2025 out. 09 ] Available from: https://doi.org/10.1007/s12355-023-01334-4
Vancouver
Maldaner LF, Molin JP, Silva ERO da. Spatial–temporal analysis to investigate the influence of in-row plant spacing on the sugarcane yield [Internet]. Sugar Tech. 2024 ; 26( 1): 194–206.[citado 2025 out. 09 ] Available from: https://doi.org/10.1007/s12355-023-01334-4
Artigo de periodico
An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology (2022)
Maldaner, Leonardo Felipe ; Canata, Tatiana Fernanda ; Molin, José Paulo
Source: Sugar Tech. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, COLHEDORAS, PROTOCOLOS DE COMUNICAÇÃO, SENSOR, TELEMETRIA
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ABNT
MALDANER, Leonardo Felipe e CANATA, Tatiana Fernanda e MOLIN, José Paulo. An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology. Sugar Tech, v. 24, n. 3, p. 813-821, 2022Tradução . . Disponível em: https://doi.org/10.1007/s12355-021-01050-x. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., Canata, T. F., & Molin, J. P. (2022). An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology. Sugar Tech, 24( 3), 813-821. doi:10.1007/s12355-021-01050-x
NLM
Maldaner LF, Canata TF, Molin JP. An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology [Internet]. Sugar Tech. 2022 ; 24( 3): 813-821.[citado 2025 out. 09 ] Available from: https://doi.org/10.1007/s12355-021-01050-x
Vancouver
Maldaner LF, Canata TF, Molin JP. An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology [Internet]. Sugar Tech. 2022 ; 24( 3): 813-821.[citado 2025 out. 09 ] Available from: https://doi.org/10.1007/s12355-021-01050-x
Artigo de periodico
Use of active sensors in coffee cultivation for monitoring crop yield (2022)
Martello, Maurício ; Molin, José Paulo ; Bazame, Helizani Couto ; Tavares, Tiago Rodrigues ; Maldaner, Leonardo Felipe
Source: Agronomy. Unidades: ESALQ, CENA
Subjects: AGRICULTURA DE PRECISÃO, CAFÉ, MONITORAMENTO, SENSORES ÓPTICOS, VARIABILIDADE ESPACIAL
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ABNT
MARTELLO, Maurício et al. Use of active sensors in coffee cultivation for monitoring crop yield. Agronomy, v. 12, p. 1-16, 2022Tradução . . Disponível em: https://doi.org/10.3390/agronomy12092118. Acesso em: 09 out. 2025.
APA
Martello, M., Molin, J. P., Bazame, H. C., Tavares, T. R., & Maldaner, L. F. (2022). Use of active sensors in coffee cultivation for monitoring crop yield. Agronomy, 12, 1-16. doi:10.3390/agronomy12092118
NLM
Martello M, Molin JP, Bazame HC, Tavares TR, Maldaner LF. Use of active sensors in coffee cultivation for monitoring crop yield [Internet]. Agronomy. 2022 ; 12 1-16.[citado 2025 out. 09 ] Available from: https://doi.org/10.3390/agronomy12092118
Vancouver
Martello M, Molin JP, Bazame HC, Tavares TR, Maldaner LF. Use of active sensors in coffee cultivation for monitoring crop yield [Internet]. Agronomy. 2022 ; 12 1-16.[citado 2025 out. 09 ] Available from: https://doi.org/10.3390/agronomy12092118
Artigo de periodico
Methodology to filter out outliers in high spatial density data to improve maps reliability (2022)
Maldaner, Leonardo Felipe ; Molin, José Paulo ; Spekken, Mark
Source: Scientia Agricola. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, AMOSTRAGEM, CANA-DE-AÇÚCAR, MAPEAMENTO DO SOLO, MILHO, SENSOR, VARIABILIDADE ESPACIAL
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ABNT
MALDANER, Leonardo Felipe e MOLIN, José Paulo e SPEKKEN, Mark. Methodology to filter out outliers in high spatial density data to improve maps reliability. Scientia Agricola, v. 79, n. 1, p. 1-7, 2022Tradução . . Disponível em: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000100102&tlng=en. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., Molin, J. P., & Spekken, M. (2022). Methodology to filter out outliers in high spatial density data to improve maps reliability. Scientia Agricola, 79( 1), 1-7. doi:10.1590/1678-992x-2020-0178
NLM
Maldaner LF, Molin JP, Spekken M. Methodology to filter out outliers in high spatial density data to improve maps reliability [Internet]. Scientia Agricola. 2022 ; 79( 1): 1-7.[citado 2025 out. 09 ] Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000100102&tlng=en
Vancouver
Maldaner LF, Molin JP, Spekken M. Methodology to filter out outliers in high spatial density data to improve maps reliability [Internet]. Scientia Agricola. 2022 ; 79( 1): 1-7.[citado 2025 out. 09 ] Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000100102&tlng=en
Artigo de periodico
A system for plant detection using sensor fusion approach based on machine learning model (2021)
Maldaner, Leonardo Felipe ; Molin, José Paulo ; Canata, Tatiana Fernanda ; Martello, Maurício
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, SENSOR
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ABNT
MALDANER, Leonardo Felipe et al. A system for plant detection using sensor fusion approach based on machine learning model. Computers and Electronics in Agriculture, v. 189, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106382. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., Molin, J. P., Canata, T. F., & Martello, M. (2021). A system for plant detection using sensor fusion approach based on machine learning model. Computers and Electronics in Agriculture, 189, 1-11. doi:10.1016/j.compag.2021.106382
NLM
Maldaner LF, Molin JP, Canata TF, Martello M. A system for plant detection using sensor fusion approach based on machine learning model [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-11.[citado 2025 out. 09 ] Available from: https://doi.org/10.1016/j.compag.2021.106382
Vancouver
Maldaner LF, Molin JP, Canata TF, Martello M. A system for plant detection using sensor fusion approach based on machine learning model [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-11.[citado 2025 out. 09 ] Available from: https://doi.org/10.1016/j.compag.2021.106382
Artigo de periodico
Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester (2021)
Lima, Jeovano de Jesus Alves de; Maldaner, Leonardo Felipe ; Molin, José Paulo
Source: Sensors. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, COLHEDORAS, REDES NEURAIS, SENSOR
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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: 09 out. 2025.
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 2025 out. 09 ] 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 2025 out. 09 ] Available from: https://doi.org/10.3390/s21134530
Artigo de periodico
Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches (2021)
Maldaner, Leonardo Felipe ; Molin, Jose Paulo ; Martello, Maurício ; Tavares, Tiago Rodrigues ; Dias, F.L.F
Source: Biosystems Engineering. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, COLHEITA, MAPEAMENTO DO SOLO, SENSOR
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MALDANER, Leonardo Felipe et al. Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches. Biosystems Engineering, v. 209, p. 64-73, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.biosystemseng.2021.06.016. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., Molin, J. P., Martello, M., Tavares, T. R., & Dias, F. L. F. (2021). Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches. Biosystems Engineering, 209, 64-73. doi:10.1016/j.biosystemseng.2021.06.016
NLM
Maldaner LF, Molin JP, Martello M, Tavares TR, Dias FLF. Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches [Internet]. Biosystems Engineering. 2021 ; 209 64-73.[citado 2025 out. 09 ] Available from: https://doi.org/10.1016/j.biosystemseng.2021.06.016
Vancouver
Maldaner LF, Molin JP, Martello M, Tavares TR, Dias FLF. Identification and measurement of gaps within sugarcane rows for site-specific management: Comparing different sensor-based approaches [Internet]. Biosystems Engineering. 2021 ; 209 64-73.[citado 2025 out. 09 ] Available from: https://doi.org/10.1016/j.biosystemseng.2021.06.016
Artigo de periodico
Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy (2021)
Corrêdo, Lucas de Paula ; Maldaner, Leonardo Felipe ; Bazame, Helizani Couto ; Molin, José Paulo
Source: Sensors. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, AMOSTRAGEM, CANA-DE-AÇÚCAR, ESPECTROSCOPIA INFRAVERMELHA, QUIMIOMETRIA, SENSOR
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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: 09 out. 2025.
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 2025 out. 09 ] 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 2025 out. 09 ] Available from: https://doi.org/10.3390/s21062195
Artigo de periodico
Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique (2021)
Canata, Tatiana Fernanda ; Wei, Marcelo Chan Fu ; Maldaner, Leonardo Felipe ; Molin, José Paulo
Source: Remote Sensing. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO
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CANATA, Tatiana Fernanda et al. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sensing, v. 13, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13020232. Acesso em: 09 out. 2025.
APA
Canata, T. F., Wei, M. C. F., Maldaner, L. F., & Molin, J. P. (2021). Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sensing, 13, 1-14. doi:10.3390/rs13020232
NLM
Canata TF, Wei MCF, Maldaner LF, Molin JP. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique [Internet]. Remote Sensing. 2021 ; 13 1-14.[citado 2025 out. 09 ] Available from: https://doi.org/10.3390/rs13020232
Vancouver
Canata TF, Wei MCF, Maldaner LF, Molin JP. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique [Internet]. Remote Sensing. 2021 ; 13 1-14.[citado 2025 out. 09 ] Available from: https://doi.org/10.3390/rs13020232
Artigo de periodico
Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches (2021)
Maldaner, Leonardo Felipe ; Corrêdo, Lucas de Paula ; Canata, Tatiana Fernanda ; Molin, José Paulo
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, COMPUTACIONAL, CANA-DE-AÇÚCAR, COLHEDORAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 09 out. 2025.
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 2025 out. 09 ] 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 2025 out. 09 ] Available from: https://doi.org/10.1016/j.compag.2020.105945
Tese (Doutorado)
Sugarcane plant detection and mapping for site-specific management (2021)
Maldaner, Leonardo Felipe ; Molin, Jose Paulo (Orientador)
Unidade: ESALQ
Subjects: AEROFOTOGRAMETRIA, AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, ESPAÇAMENTO, SENSOR
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ABNT
MALDANER, Leonardo Felipe. Sugarcane plant detection and mapping for site-specific management. 2021. Tese (Doutorado) – Universidade de São Paulo, Piracicaba, 2021. Disponível em: https://www.teses.usp.br/teses/disponiveis/11/11152/tde-14022022-164102/. Acesso em: 09 out. 2025.
APA
Maldaner, L. F. (2021). Sugarcane plant detection and mapping for site-specific management (Tese (Doutorado). Universidade de São Paulo, Piracicaba. Recuperado de https://www.teses.usp.br/teses/disponiveis/11/11152/tde-14022022-164102/
NLM
Maldaner LF. Sugarcane plant detection and mapping for site-specific management [Internet]. 2021 ;[citado 2025 out. 09 ] Available from: https://www.teses.usp.br/teses/disponiveis/11/11152/tde-14022022-164102/
Vancouver
Maldaner LF. Sugarcane plant detection and mapping for site-specific management [Internet]. 2021 ;[citado 2025 out. 09 ] Available from: https://www.teses.usp.br/teses/disponiveis/11/11152/tde-14022022-164102/
Artigo de periodico
3D data processing to characterize the spatial variability of sugarcane fields (2021)
Canata, Tatiana Fernanda ; Martello, Maurício ; Maldaner, Leonardo Felipe ; Moreira, Jadir de Souza; Molin, José Paulo
Source: Sugar Tech. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, IMAGEM 3D, PROCESSAMENTO DE DADOS, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR, VARIABILIDADE ESPACIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CANATA, Tatiana Fernanda et al. 3D data processing to characterize the spatial variability of sugarcane fields. Sugar Tech, 2021Tradução . . Disponível em: https://doi.org/10.1007/s12355-021-01048-5. Acesso em: 09 out. 2025.
APA
Canata, T. F., Martello, M., Maldaner, L. F., Moreira, J. de S., & Molin, J. P. (2021). 3D data processing to characterize the spatial variability of sugarcane fields. Sugar Tech. doi:10.1007/s12355-021-01048-5
NLM
Canata TF, Martello M, Maldaner LF, Moreira J de S, Molin JP. 3D data processing to characterize the spatial variability of sugarcane fields [Internet]. Sugar Tech. 2021 ;[citado 2025 out. 09 ] Available from: https://doi.org/10.1007/s12355-021-01048-5
Vancouver
Canata TF, Martello M, Maldaner LF, Moreira J de S, Molin JP. 3D data processing to characterize the spatial variability of sugarcane fields [Internet]. Sugar Tech. 2021 ;[citado 2025 out. 09 ] Available from: https://doi.org/10.1007/s12355-021-01048-5
Artigo de periodico
A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations (2021)
Maldaner, Leonardo Felipe ; Canata, Tatiana Fernanda ; Dias, Carlos Tadeu dos Santos ; Molin, José Paulo
Source: Scientia Agricola. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, GEOESTATÍSTICA, SATÉLITES ARTIFICIAIS, SISTEMA DE POSICIONAMENTO GLOBAL
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MALDANER, Leonardo Felipe et al. A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations. Scientia Agricola, v. 78, n. 5, p. 1-9, 2021Tradução . . Disponível em: https://doi.org/10.1590/1678-992x-2019-0252. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., Canata, T. F., Dias, C. T. dos S., & Molin, J. P. (2021). A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations. Scientia Agricola, 78( 5), 1-9. doi:10.1590/1678-992x-2019-0252
NLM
Maldaner LF, Canata TF, Dias CT dos S, Molin JP. A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations [Internet]. Scientia Agricola. 2021 ; 78( 5): 1-9.[citado 2025 out. 09 ] Available from: https://doi.org/10.1590/1678-992x-2019-0252
Vancouver
Maldaner LF, Canata TF, Dias CT dos S, Molin JP. A statistical approach to static and dynamic tests for Global Navigation Satellite Systems receivers used in agricultural operations [Internet]. Scientia Agricola. 2021 ; 78( 5): 1-9.[citado 2025 out. 09 ] Available from: https://doi.org/10.1590/1678-992x-2019-0252
Artigo de periodico
Carrot yield mapping: a precision agriculture approach based on machine learning (2020)
Wei, Marcelo Chan Fu ; Maldaner, Leonardo Felipe ; Ottoni, Pedro Medeiros Netto; Molin, José Paulo
Source: AI. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CENOURA, IMAGEAMENTO DE SATÉLITE, MAPAS, SENSORIAMENTO REMOTO
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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: 09 out. 2025.
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 2025 out. 09 ] 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 2025 out. 09 ] Available from: https://doi.org/10.3390/ai1020015
Artigo de periodico
Sugarcane Harvester for In-field Data Collection:: State of the Art, Its Applicability and Future Perspectives (2020)
Corrêdo, Lucas de Paula ; Canata, Tatiana Fernanda ; Maldaner, Leonardo Felipe ; Lima, Jeovano de Jesus Alves de; Molin, José Paulo
Source: Sugar Tech. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, COLHEDORAS, SENSOR, VARIABILIDADE ESPACIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 09 out. 2025.
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 2025 out. 09 ] 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 2025 out. 09 ] Available from: https://doi.org/10.1007/s12355-020-00874-3
Artigo de periodico
High-resolution imagery data to assess the spatial variability of sugarcane fields (2020)
Pissolito, João Pedro; Bersani, Victor Hugo Sousa; Canata, Tatiana Fernanda ; Maldaner, Leonardo Felipe ; Molin, José Paulo
Source: Brazilian Journal of Development. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, CANA-DE-AÇÚCAR, SENSORIAMENTO REMOTO, VARIABILIDADE ESPACIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PISSOLITO, João Pedro et al. High-resolution imagery data to assess the spatial variability of sugarcane fields. Brazilian Journal of Development, v. 6, n. 12, p. 100266-100280, 2020Tradução . . Disponível em: https://doi.org/10.34117/bjdv6n12-500. Acesso em: 09 out. 2025.
APA
Pissolito, J. P., Bersani, V. H. S., Canata, T. F., Maldaner, L. F., & Molin, J. P. (2020). High-resolution imagery data to assess the spatial variability of sugarcane fields. Brazilian Journal of Development, 6( 12), 100266-100280. doi:10.34117/bjdv6n12-500
NLM
Pissolito JP, Bersani VHS, Canata TF, Maldaner LF, Molin JP. High-resolution imagery data to assess the spatial variability of sugarcane fields [Internet]. Brazilian Journal of Development. 2020 ; 6( 12): 100266-100280.[citado 2025 out. 09 ] Available from: https://doi.org/10.34117/bjdv6n12-500
Vancouver
Pissolito JP, Bersani VHS, Canata TF, Maldaner LF, Molin JP. High-resolution imagery data to assess the spatial variability of sugarcane fields [Internet]. Brazilian Journal of Development. 2020 ; 6( 12): 100266-100280.[citado 2025 out. 09 ] Available from: https://doi.org/10.34117/bjdv6n12-500
Artigo de periodico
Data processing within rows for sugarcane yield mapping (2020)
Maldaner, Leonardo Felipe ; Molin, Jose Paulo
Source: Scientia Agricola. Unidade: ESALQ
Subjects: CANA-DE-AÇÚCAR, MAPEAMENTO DO SOLO, PROCESSAMENTO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MALDANER, Leonardo Felipe e MOLIN, Jose Paulo. Data processing within rows for sugarcane yield mapping. Scientia Agricola, v. 77, n. 5, p. 1-8, 2020Tradução . . Disponível em: https://doi.org/10.1590/1678-992X-2018-0391. Acesso em: 09 out. 2025.
APA
Maldaner, L. F., & Molin, J. P. (2020). Data processing within rows for sugarcane yield mapping. Scientia Agricola, 77( 5), 1-8. doi:10.1590/1678-992X-2018-0391
NLM
Maldaner LF, Molin JP. Data processing within rows for sugarcane yield mapping [Internet]. Scientia Agricola. 2020 ; 77( 5): 1-8.[citado 2025 out. 09 ] Available from: https://doi.org/10.1590/1678-992X-2018-0391
Vancouver
Maldaner LF, Molin JP. Data processing within rows for sugarcane yield mapping [Internet]. Scientia Agricola. 2020 ; 77( 5): 1-8.[citado 2025 out. 09 ] Available from: https://doi.org/10.1590/1678-992X-2018-0391
Artigo de periodico
Precision agriculture and the digital contributions for site-specific management of the fields (2020)
Molin, Jose Paulo ; Bazame, Helizani Couto ; Maldaner, Leonardo Felipe ; Corredo, Lucas de Paula ; Martello, Mauricio ; Canata, Tatiana Fernanda
Source: Revista Ciência Agronômica. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, COLETA DE DADOS, COMPUTAÇÃO EM NUVEM, INTELIGÊNCIA ARTIFICIAL, INTERNET DAS COISAS, PRODUÇÃO AGRÍCOLA, SISTEMAS DE APOIO À DECISÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MOLIN, Jose Paulo et al. Precision agriculture and the digital contributions for site-specific management of the fields. Revista Ciência Agronômica, v. 51, n. Special Agriculture 4.0, p. 1-10, 2020Tradução . . Disponível em: https://doi.org/10.5935/1806-6690.20200088. Acesso em: 09 out. 2025.
APA
Molin, J. P., Bazame, H. C., Maldaner, L. F., Corredo, L. de P., Martello, M., & Canata, T. F. (2020). Precision agriculture and the digital contributions for site-specific management of the fields. Revista Ciência Agronômica, 51( Special Agriculture 4.0), 1-10. doi:10.5935/1806-6690.20200088
NLM
Molin JP, Bazame HC, Maldaner LF, Corredo L de P, Martello M, Canata TF. Precision agriculture and the digital contributions for site-specific management of the fields [Internet]. Revista Ciência Agronômica. 2020 ; 51( Special Agriculture 4.0): 1-10.[citado 2025 out. 09 ] Available from: https://doi.org/10.5935/1806-6690.20200088
Vancouver
Molin JP, Bazame HC, Maldaner LF, Corredo L de P, Martello M, Canata TF. Precision agriculture and the digital contributions for site-specific management of the fields [Internet]. Revista Ciência Agronômica. 2020 ; 51( Special Agriculture 4.0): 1-10.[citado 2025 out. 09 ] Available from: https://doi.org/10.5935/1806-6690.20200088
Trabalho de evento
Performance analysis of a RADAR FMCW sensor developed for plant height measurement in agricultural and comparison with an NDVI sensor (2019)
Santos, Pedro Henrique; Gimenez, Leandro Maria ; Maldaner, Leonardo Felipe ; Duarte, Cássio da Costa
Source: Proceedings. Conference titles: Brazilian Symposium on GeoInformatics - GEOINFO. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, MILHETO, IMAGEAMENTO DE RADAR, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Pedro Henrique et al. Performance analysis of a RADAR FMCW sensor developed for plant height measurement in agricultural and comparison with an NDVI sensor. 2019, Anais.. São José dos Campos, SP: MCTIC/INPE, 2019. Disponível em: http://mtc-m21c.sid.inpe.br/col/sid.inpe.br/mtc-m21c/2019/12.10.14.01/doc/proceedings_2019_compressed.pdf. Acesso em: 09 out. 2025.
APA
Santos, P. H., Gimenez, L. M., Maldaner, L. F., & Duarte, C. da C. (2019). Performance analysis of a RADAR FMCW sensor developed for plant height measurement in agricultural and comparison with an NDVI sensor. In Proceedings. São José dos Campos, SP: MCTIC/INPE. Recuperado de http://mtc-m21c.sid.inpe.br/col/sid.inpe.br/mtc-m21c/2019/12.10.14.01/doc/proceedings_2019_compressed.pdf
NLM
Santos PH, Gimenez LM, Maldaner LF, Duarte C da C. Performance analysis of a RADAR FMCW sensor developed for plant height measurement in agricultural and comparison with an NDVI sensor [Internet]. Proceedings. 2019 ;[citado 2025 out. 09 ] Available from: http://mtc-m21c.sid.inpe.br/col/sid.inpe.br/mtc-m21c/2019/12.10.14.01/doc/proceedings_2019_compressed.pdf
Vancouver
Santos PH, Gimenez LM, Maldaner LF, Duarte C da C. Performance analysis of a RADAR FMCW sensor developed for plant height measurement in agricultural and comparison with an NDVI sensor [Internet]. Proceedings. 2019 ;[citado 2025 out. 09 ] Available from: http://mtc-m21c.sid.inpe.br/col/sid.inpe.br/mtc-m21c/2019/12.10.14.01/doc/proceedings_2019_compressed.pdf

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