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Artigo de periodico
Integration of ultrasonic and optical sensing systems to assess sugarcane biomass and N-uptake (2024)
Portz, Gustavo; Molin, José Paulo ; Canata, Tatiana Fernanda ; Adamchuk, V. L
Source: Precision Agriculture. Unidade: ESALQ
Subjects: BIOMASSA, CANA-DE-AÇÚCAR, DOSSEL (BOTÂNICA), FERTILIZANTES NITROGENADOS, NITROGÊNIO, SENSORIAMENTO REMOTO
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ABNT
PORTZ, Gustavo et al. Integration of ultrasonic and optical sensing systems to assess sugarcane biomass and N-uptake. Precision Agriculture, v. 25, p. 83–99, 2024Tradução . . Disponível em: https://doi.org/10.1007/s11119-023-10059-z. Acesso em: 24 jun. 2024.
APA
Portz, G., Molin, J. P., Canata, T. F., & Adamchuk, V. L. (2024). Integration of ultrasonic and optical sensing systems to assess sugarcane biomass and N-uptake. Precision Agriculture, 25, 83–99. doi:10.1007/s11119-023-10059-z
NLM
Portz G, Molin JP, Canata TF, Adamchuk VL. Integration of ultrasonic and optical sensing systems to assess sugarcane biomass and N-uptake [Internet]. Precision Agriculture. 2024 ; 25 83–99.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1007/s11119-023-10059-z
Vancouver
Portz G, Molin JP, Canata TF, Adamchuk VL. Integration of ultrasonic and optical sensing systems to assess sugarcane biomass and N-uptake [Internet]. Precision Agriculture. 2024 ; 25 83–99.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1007/s11119-023-10059-z
Artigo de periodico
Detection of coffee fruits on tree branches using computer vision (2023)
Bazame, Helizani Couto ; Molin, José Paulo ; Althoff, Daniel ; Martello, Maurício
Source: Scientia Agricola. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, ALGORITMOS, APRENDIZADO COMPUTACIONAL, CAFÉ, FRUTO, MATURAÇÃO VEGETAL, VISÃO COMPUTACIONAL
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ABNT
BAZAME, Helizani Couto et al. Detection of coffee fruits on tree branches using computer vision. Scientia Agricola, v. 80, p. 1-8, 2023Tradução . . Disponível em: https://doi.org/10.1590/1678-992X-2022-0064. Acesso em: 24 jun. 2024.
APA
Bazame, H. C., Molin, J. P., Althoff, D., & Martello, M. (2023). Detection of coffee fruits on tree branches using computer vision. Scientia Agricola, 80, 1-8. doi:10.1590/1678-992X-2022-0064
NLM
Bazame HC, Molin JP, Althoff D, Martello M. Detection of coffee fruits on tree branches using computer vision [Internet]. Scientia Agricola. 2023 ; 80 1-8.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1590/1678-992X-2022-0064
Vancouver
Bazame HC, Molin JP, Althoff D, Martello M. Detection of coffee fruits on tree branches using computer vision [Internet]. Scientia Agricola. 2023 ; 80 1-8.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1590/1678-992X-2022-0064
Artigo de periodico
Towards rapid analysis with XRF sensor for assessing soil fertility attributes: effects of dwell time reduction (2023)
Tavares, Tiago Rodrigues; Molin, José Paulo ; Alves, Elton Eduardo Novais ; Melquiades, Fabio Luiz ; Carvalho, Hudson Wallace Pereira de ; Mouazen, Abdul Mounem
Source: Soil & Tillage Research. Unidades: ESALQ, CENA
Subjects: AGRICULTURA DE PRECISÃO, ANÁLISE DO SOLO, ESPECTROSCOPIA DE RAIO X, FLUORESCÊNCIA, FERTILIDADE DO SOLO, MANEJO DO SOLO, SENSOR
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ABNT
TAVARES, Tiago Rodrigues et al. Towards rapid analysis with XRF sensor for assessing soil fertility attributes: effects of dwell time reduction. Soil & Tillage Research, v. 232, p. 1-10, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.still.2023.105768. Acesso em: 24 jun. 2024.
APA
Tavares, T. R., Molin, J. P., Alves, E. E. N., Melquiades, F. L., Carvalho, H. W. P. de, & Mouazen, A. M. (2023). Towards rapid analysis with XRF sensor for assessing soil fertility attributes: effects of dwell time reduction. Soil & Tillage Research, 232, 1-10. doi:10.1016/j.still.2023.105768
NLM
Tavares TR, Molin JP, Alves EEN, Melquiades FL, Carvalho HWP de, Mouazen AM. Towards rapid analysis with XRF sensor for assessing soil fertility attributes: effects of dwell time reduction [Internet]. Soil & Tillage Research. 2023 ; 232 1-10.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1016/j.still.2023.105768
Vancouver
Tavares TR, Molin JP, Alves EEN, Melquiades FL, Carvalho HWP de, Mouazen AM. Towards rapid analysis with XRF sensor for assessing soil fertility attributes: effects of dwell time reduction [Internet]. Soil & Tillage Research. 2023 ; 232 1-10.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1016/j.still.2023.105768
Artigo de periodico
Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): a dataset for soil fertility prediction (2022)
Tavares, Tiago Rodrigues ; Molin, José Paulo ; Nunes, Lidiane Cristina ; Alves, Elton Eduardo Novais ; Krug, Francisco José ; Carvalho, Hudson Wallace Pereira de
Source: Data in Brief. Unidades: ESALQ, CENA
Subjects: AGRICULTURA DE PRECISÃO, ESPECTROSCOPIA, FERTILIDADE DO SOLO, SENSORES QUÍMICOS, SOLO TROPICAL
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ABNT
TAVARES, Tiago Rodrigues et al. Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): a dataset for soil fertility prediction. Data in Brief, v. 41, p. 1-7, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.dib.2022.108004. Acesso em: 24 jun. 2024.
APA
Tavares, T. R., Molin, J. P., Nunes, L. C., Alves, E. E. N., Krug, F. J., & Carvalho, H. W. P. de. (2022). Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): a dataset for soil fertility prediction. Data in Brief, 41, 1-7. doi:10.1016/j.dib.2022.108004
NLM
Tavares TR, Molin JP, Nunes LC, Alves EEN, Krug FJ, Carvalho HWP de. Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): a dataset for soil fertility prediction [Internet]. Data in Brief. 2022 ; 41 1-7.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1016/j.dib.2022.108004
Vancouver
Tavares TR, Molin JP, Nunes LC, Alves EEN, Krug FJ, Carvalho HWP de. Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): a dataset for soil fertility prediction [Internet]. Data in Brief. 2022 ; 41 1-7.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1016/j.dib.2022.108004
Artigo de periodico
Mapping coffee yield with computer vision (2022)
Bazame, Helizani Couto ; Molin, José Paulo ; Althoff, Daniel; Martello, Maurício ; Corrêdo, Lucas de Paula
Source: Precision Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CAFÉ, COLHEDORAS, VISÃO COMPUTACIONAL
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ABNT
BAZAME, Helizani Couto et al. Mapping coffee yield with computer vision. Precision Agriculture, p. 1-16, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11119-022-09924-0. Acesso em: 24 jun. 2024.
APA
Bazame, H. C., Molin, J. P., Althoff, D., Martello, M., & Corrêdo, L. de P. (2022). Mapping coffee yield with computer vision. Precision Agriculture, 1-16. doi:10.1007/s11119-022-09924-0
NLM
Bazame HC, Molin JP, Althoff D, Martello M, Corrêdo L de P. Mapping coffee yield with computer vision [Internet]. Precision Agriculture. 2022 ; 1-16.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1007/s11119-022-09924-0
Vancouver
Bazame HC, Molin JP, Althoff D, Martello M, Corrêdo L de P. Mapping coffee yield with computer vision [Internet]. Precision Agriculture. 2022 ; 1-16.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1007/s11119-022-09924-0
Artigo de periodico
Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy (2022)
Canal Filho, Ricardo ; Molin, José Paulo
Source: Frontiers in Soil Science. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, DISTRIBUIÇÃO ESPACIAL, ESPECTROSCOPIA INFRAVERMELHA, MANEJO DO SOLO, SOLOS
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ABNT
CANAL FILHO, Ricardo e MOLIN, José Paulo. Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy. Frontiers in Soil Science, v. 2, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.3389/fsoil.2022.984963. Acesso em: 24 jun. 2024.
APA
Canal Filho, R., & Molin, J. P. (2022). Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy. Frontiers in Soil Science, 2, 1-13. doi:10.3389/fsoil.2022.984963
NLM
Canal Filho R, Molin JP. Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy [Internet]. Frontiers in Soil Science. 2022 ; 2 1-13.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3389/fsoil.2022.984963
Vancouver
Canal Filho R, Molin JP. Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy [Internet]. Frontiers in Soil Science. 2022 ; 2 1-13.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3389/fsoil.2022.984963
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: 24 jun. 2024.
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 2024 jun. 24 ] 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 2024 jun. 24 ] Available from: https://doi.org/10.1007/s12355-021-01050-x
Artigo de periodico
Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data (2022)
Wei, Marcelo Chan Fu ; Canal Filho, Ricardo ; Tavares, Tiago Rodrigues ; Molin, José Paulo ; Vieira, Afrânio Márcio Corrêa
Source: AI. Unidades: ESALQ, CENA
Subjects: ANÁLISE MULTIVARIADA, ESPECTROSCOPIA, FLUORESCÊNCIA, RAIOS X, INFRAVERMELHO, MODELOS MATEMÁTICOS, SOLOS
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ABNT
WEI, Marcelo Chan Fu et al. Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data. AI, v. 3, p. 809-819, 2022Tradução . . Disponível em: https://doi.org/10.3390/ai3040049. Acesso em: 24 jun. 2024.
APA
Wei, M. C. F., Canal Filho, R., Tavares, T. R., Molin, J. P., & Vieira, A. M. C. (2022). Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data. AI, 3, 809-819. doi:10.3390/ai3040049
NLM
Wei MCF, Canal Filho R, Tavares TR, Molin JP, Vieira AMC. Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data [Internet]. AI. 2022 ; 3 809-819.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/ai3040049
Vancouver
Wei MCF, Canal Filho R, Tavares TR, Molin JP, Vieira AMC. Dimensionality reduction statistical models for soil attribute prediction based on raw spectral data [Internet]. AI. 2022 ; 3 809-819.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/ai3040049
Artigo de periodico
Coffee-yield estimation using high-resolution time-series satellite images and machine learning (2022)
Martello, Maurício ; Molin, José Paulo ; Wei, Marcelo Chan Fu ; Canal Filho, Ricardo ; Nicoletti, João Vitor Moreira
Source: AgriEngineering. Unidades: ESALQ, ICMC
Subjects: AGRICULTURA DE PRECISÃO, ANÁLISE DE SÉRIES TEMPORAIS, APRENDIZADO COMPUTACIONAL, CAFÉ, IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO
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ABNT
MARTELLO, Maurício et al. Coffee-yield estimation using high-resolution time-series satellite images and machine learning. AgriEngineering, v. 4, p. 888-902, 2022Tradução . . Disponível em: https://doi.org/10.3390/agriengineering4040057. Acesso em: 24 jun. 2024.
APA
Martello, M., Molin, J. P., Wei, M. C. F., Canal Filho, R., & Nicoletti, J. V. M. (2022). Coffee-yield estimation using high-resolution time-series satellite images and machine learning. AgriEngineering, 4, 888-902. doi:10.3390/agriengineering4040057
NLM
Martello M, Molin JP, Wei MCF, Canal Filho R, Nicoletti JVM. Coffee-yield estimation using high-resolution time-series satellite images and machine learning [Internet]. AgriEngineering. 2022 ; 4 888-902.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agriengineering4040057
Vancouver
Martello M, Molin JP, Wei MCF, Canal Filho R, Nicoletti JVM. Coffee-yield estimation using high-resolution time-series satellite images and machine learning [Internet]. AgriEngineering. 2022 ; 4 888-902.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agriengineering4040057
Artigo de periodico
Precision agriculture in Brazil: the trajectory of 25 years of scientific research (2022)
Cherubin, Maurício Roberto ; Damian, Júnior Melo ; Tavares, Tiago Rodrigues ; Trevisan, Rodrigo Gonçalves; Colaço, André Freitas; Eitelwein, Mateus Tonini; Martello, Maurício ; Inamasu, Ricardo Yassushi; Pias, Osmar Henrique de Castro; Molin, José Paulo
Source: Agriculture. Unidades: ESALQ, CENA
Subjects: AGRICULTURA DE PRECISÃO, PESQUISA CIENTÍFICA
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ABNT
CHERUBIN, Maurício Roberto et al. Precision agriculture in Brazil: the trajectory of 25 years of scientific research. Agriculture, p. 1-29, 2022Tradução . . Disponível em: https://doi.org/10.3390/agriculture12111882. Acesso em: 24 jun. 2024.
APA
Cherubin, M. R., Damian, J. M., Tavares, T. R., Trevisan, R. G., Colaço, A. F., Eitelwein, M. T., et al. (2022). Precision agriculture in Brazil: the trajectory of 25 years of scientific research. Agriculture, 1-29. doi:10.3390/agriculture12111882
NLM
Cherubin MR, Damian JM, Tavares TR, Trevisan RG, Colaço AF, Eitelwein MT, Martello M, Inamasu RY, Pias OH de C, Molin JP. Precision agriculture in Brazil: the trajectory of 25 years of scientific research [Internet]. Agriculture. 2022 ; 1-29.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agriculture12111882
Vancouver
Cherubin MR, Damian JM, Tavares TR, Trevisan RG, Colaço AF, Eitelwein MT, Martello M, Inamasu RY, Pias OH de C, Molin JP. Precision agriculture in Brazil: the trajectory of 25 years of scientific research [Internet]. Agriculture. 2022 ; 1-29.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agriculture12111882
Artigo de periodico
Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN (2022)
Eitelwein, Mateus Tonini; Tavares, Tiago Rodrigues ; Molin, José Paulo ; Trevisan, Rodrigo Gonçalves; Sousa, Rafael Vieira de ; Demattê, José Alexandre Melo
Source: Automation. Unidades: ESALQ, FZEA, CENA
Subjects: AGRICULTURA DE PRECISÃO, ESPECTROSCOPIA INFRAVERMELHA, FERTILIDADE DO SOLO, MAPEAMENTO DO SOLO, MODELAGEM DE DADOS, SENSOR, SOLO TROPICAL
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ABNT
EITELWEIN, Mateus Tonini et al. Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN. Automation, v. 3, p. 116-131, 2022Tradução . . Disponível em: https://doi.org/10.3390/automation3010006. Acesso em: 24 jun. 2024.
APA
Eitelwein, M. T., Tavares, T. R., Molin, J. P., Trevisan, R. G., Sousa, R. V. de, & Demattê, J. A. M. (2022). Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN. Automation, 3, 116-131. doi:10.3390/automation3010006
NLM
Eitelwein MT, Tavares TR, Molin JP, Trevisan RG, Sousa RV de, Demattê JAM. Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN [Internet]. Automation. 2022 ; 3 116-131.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/automation3010006
Vancouver
Eitelwein MT, Tavares TR, Molin JP, Trevisan RG, Sousa RV de, Demattê JAM. Predictive performance of mobile Vis–NIR spectroscopy for mapping key fertility attributes in tropical soils through local models using PLS and ANN [Internet]. Automation. 2022 ; 3 116-131.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/automation3010006
Artigo de periodico
Laser-Induced Breakdown Spectroscopy (LIBS) for tropical soil fertility analysis (2022)
Tavares, Tiago R ; Mouazen, Abdul M; Nunes, Lidiane C ; Santos, Felipe R. dos; Melquiades, Fábio L; Silva, Thainara R. da ; Krug, Francisco José ; Molin, José Paulo
Source: Soil & Tillage Research. Unidades: CENA, ESALQ
Subjects: AGRICULTURA DE PRECISÃO, ESPECTROSCOPIA, LASER, FERTILIDADE DO SOLO, SOLO TROPICAL
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ABNT
TAVARES, Tiago R et al. Laser-Induced Breakdown Spectroscopy (LIBS) for tropical soil fertility analysis. Soil & Tillage Research, v. 216, p. 1-12, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.still.2021.105250. Acesso em: 24 jun. 2024.
APA
Tavares, T. R., Mouazen, A. M., Nunes, L. C., Santos, F. R. dos, Melquiades, F. L., Silva, T. R. da, et al. (2022). Laser-Induced Breakdown Spectroscopy (LIBS) for tropical soil fertility analysis. Soil & Tillage Research, 216, 1-12. doi:10.1016/j.still.2021.105250
NLM
Tavares TR, Mouazen AM, Nunes LC, Santos FR dos, Melquiades FL, Silva TR da, Krug FJ, Molin JP. Laser-Induced Breakdown Spectroscopy (LIBS) for tropical soil fertility analysis [Internet]. Soil & Tillage Research. 2022 ; 216 1-12.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1016/j.still.2021.105250
Vancouver
Tavares TR, Mouazen AM, Nunes LC, Santos FR dos, Melquiades FL, Silva TR da, Krug FJ, Molin JP. Laser-Induced Breakdown Spectroscopy (LIBS) for tropical soil fertility analysis [Internet]. Soil & Tillage Research. 2022 ; 216 1-12.[citado 2024 jun. 24 ] Available from: https://doi.org/10.1016/j.still.2021.105250
Artigo de periodico
Measuring apparent electrical conductivity in undisturbed soil samples (2021)
Gimenez, Leandro Maria ; Molin, José Paulo
Source: Brazilian Journal of Development. Unidade: ESALQ
Subjects: AMOSTRAGEM, CONDUTIVIDADE ELÉTRICA, GRANULOMETRIA DO SOLO, UMIDADE DO SOLO
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ABNT
GIMENEZ, Leandro Maria e MOLIN, José Paulo. Measuring apparent electrical conductivity in undisturbed soil samples. Brazilian Journal of Development, v. 7, n. 7, p. 73620-73632, 2021Tradução . . Disponível em: https://doi.org/10.34117/bjdv7n7-506. Acesso em: 24 jun. 2024.
APA
Gimenez, L. M., & Molin, J. P. (2021). Measuring apparent electrical conductivity in undisturbed soil samples. Brazilian Journal of Development, 7( 7), 73620-73632. doi:10.34117/bjdv7n7-506
NLM
Gimenez LM, Molin JP. Measuring apparent electrical conductivity in undisturbed soil samples [Internet]. Brazilian Journal of Development. 2021 ; 7( 7): 73620-73632.[citado 2024 jun. 24 ] Available from: https://doi.org/10.34117/bjdv7n7-506
Vancouver
Gimenez LM, Molin JP. Measuring apparent electrical conductivity in undisturbed soil samples [Internet]. Brazilian Journal of Development. 2021 ; 7( 7): 73620-73632.[citado 2024 jun. 24 ] Available from: https://doi.org/10.34117/bjdv7n7-506
Artigo de periodico
Energy efficiency of variable rate fertilizer application in coffee production in Brazil (2021)
Angnes, Graciele ; Martello, Maurício ; Faulin, Gustavo Di Chiacchio; Molin, José Paulo ; Romanelli, Thiago Libório
Source: AgriEngineering. Unidade: ESALQ
Subjects: CAFÉ, EFICIÊNCIA ENERGÉTICA, CAFEICULTURA, FERTILIZAÇÃO, FERTILIZANTES, AGRICULTURA DE PRECISÃO
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ABNT
ANGNES, Graciele et al. Energy efficiency of variable rate fertilizer application in coffee production in Brazil. AgriEngineering, v. 3, p. 815-826, 2021Tradução . . Disponível em: https://doi.org/10.3390/agriengineering3040051. Acesso em: 24 jun. 2024.
APA
Angnes, G., Martello, M., Faulin, G. D. C., Molin, J. P., & Romanelli, T. L. (2021). Energy efficiency of variable rate fertilizer application in coffee production in Brazil. AgriEngineering, 3, 815-826. doi:10.3390/agriengineering3040051
NLM
Angnes G, Martello M, Faulin GDC, Molin JP, Romanelli TL. Energy efficiency of variable rate fertilizer application in coffee production in Brazil [Internet]. AgriEngineering. 2021 ; 3 815-826.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agriengineering3040051
Vancouver
Angnes G, Martello M, Faulin GDC, Molin JP, Romanelli TL. Energy efficiency of variable rate fertilizer application in coffee production in Brazil [Internet]. AgriEngineering. 2021 ; 3 815-826.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agriengineering3040051
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: 24 jun. 2024.
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 2024 jun. 24 ] 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 2024 jun. 24 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 24 jun. 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 jun. 24 ] 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 jun. 24 ] Available from: https://doi.org/10.3390/s21134530
Artigo de periodico
Multi-sensor approach for tropical soil fertility analysis: comparison of individual and combined performance of VNIR, XRF, and LIBS spectroscopies (2021)
Tavares, Tiago Rodrigues ; Molin, José Paulo ; Nunes, Lidiane Cristina ; Wei, Marcelo Chan Fu ; Krug, Francisco José ; Carvalho, Hudson Wallace Pereira de ; Mouazen, Abdul Mounem
Source: Agronomy. Unidades: ESALQ, CENA
Subjects: FERTILIDADE DO SOLO, SOLO TROPICAL, SENSOR, ESPECTROSCOPIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TAVARES, Tiago Rodrigues et al. Multi-sensor approach for tropical soil fertility analysis: comparison of individual and combined performance of VNIR, XRF, and LIBS spectroscopies. Agronomy, v. 11, p. 1-18, 2021Tradução . . Disponível em: https://doi.org/10.3390/agronomy11061028. Acesso em: 24 jun. 2024.
APA
Tavares, T. R., Molin, J. P., Nunes, L. C., Wei, M. C. F., Krug, F. J., Carvalho, H. W. P. de, & Mouazen, A. M. (2021). Multi-sensor approach for tropical soil fertility analysis: comparison of individual and combined performance of VNIR, XRF, and LIBS spectroscopies. Agronomy, 11, 1-18. doi:10.3390/agronomy11061028
NLM
Tavares TR, Molin JP, Nunes LC, Wei MCF, Krug FJ, Carvalho HWP de, Mouazen AM. Multi-sensor approach for tropical soil fertility analysis: comparison of individual and combined performance of VNIR, XRF, and LIBS spectroscopies [Internet]. Agronomy. 2021 ; 11 1-18.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agronomy11061028
Vancouver
Tavares TR, Molin JP, Nunes LC, Wei MCF, Krug FJ, Carvalho HWP de, Mouazen AM. Multi-sensor approach for tropical soil fertility analysis: comparison of individual and combined performance of VNIR, XRF, and LIBS spectroscopies [Internet]. Agronomy. 2021 ; 11 1-18.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/agronomy11061028
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
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: 24 jun. 2024.
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 2024 jun. 24 ] 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 2024 jun. 24 ] Available from: https://doi.org/10.3390/s21062195
Parte de monografia/livro
Applications of optical sensing of crop health and vigour (2021)
Taylor, J. A; Anastasiou, E; Fountas, S; Tisseyre, B; Molin, José Paulo ; Trevisan, Rodrigo Gonçalves; Chen, H; Travers, M
Source: Sensing approaches for precision agriculture. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, ALGODÃO, BATATA, COLHEITA, DOSSEL (BOTÂNICA), SENSORES ÓPTICOS, TECNOLOGIA LIDAR, UVA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TAYLOR, J. A et al. Applications of optical sensing of crop health and vigour. Sensing approaches for precision agriculture. Tradução . Cham: Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, 2021. . Disponível em: https://doi.org/10.1007/978-3-030-78431-7. Acesso em: 24 jun. 2024.
APA
Taylor, J. A., Anastasiou, E., Fountas, S., Tisseyre, B., Molin, J. P., Trevisan, R. G., et al. (2021). Applications of optical sensing of crop health and vigour. In Sensing approaches for precision agriculture. Cham: Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo. doi:10.1007/978-3-030-78431-7
NLM
Taylor JA, Anastasiou E, Fountas S, Tisseyre B, Molin JP, Trevisan RG, Chen H, Travers M. Applications of optical sensing of crop health and vigour [Internet]. In: Sensing approaches for precision agriculture. Cham: Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo; 2021. [citado 2024 jun. 24 ] Available from: https://doi.org/10.1007/978-3-030-78431-7
Vancouver
Taylor JA, Anastasiou E, Fountas S, Tisseyre B, Molin JP, Trevisan RG, Chen H, Travers M. Applications of optical sensing of crop health and vigour [Internet]. In: Sensing approaches for precision agriculture. Cham: Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo; 2021. [citado 2024 jun. 24 ] Available from: https://doi.org/10.1007/978-3-030-78431-7
Artigo de periodico
Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches (2021)
Tavares, Tiago Rodrigues ; Molin, José Paulo ; Javadi, S. Hamed ; Carvalho, Hudson Wallace Pereira de ; Mouazen, Abdul Mounem
Source: Sensors. Unidades: ESALQ, CENA
Subjects: AGRICULTURA DE PRECISÃO, ESPECTROSCOPIA INFRAVERMELHA, FERTILIDADE DO SOLO, FLUORESCÊNCIA, RAIOS X, SENSOR, SOLO TROPICAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TAVARES, Tiago Rodrigues et al. Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches. Sensors, v. 21, p. 493-501, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21010148. Acesso em: 24 jun. 2024.
APA
Tavares, T. R., Molin, J. P., Javadi, S. H., Carvalho, H. W. P. de, & Mouazen, A. M. (2021). Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches. Sensors, 21, 493-501. doi:10.3390/s21010148
NLM
Tavares TR, Molin JP, Javadi SH, Carvalho HWP de, Mouazen AM. Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches [Internet]. Sensors. 2021 ; 21 493-501.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/s21010148
Vancouver
Tavares TR, Molin JP, Javadi SH, Carvalho HWP de, Mouazen AM. Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis: assessing different data fusion approaches [Internet]. Sensors. 2021 ; 21 493-501.[citado 2024 jun. 24 ] Available from: https://doi.org/10.3390/s21010148

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