ReP USP - Resultado da busca

Artigo de periodico
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion (2025)
Zielinski, Kallil Miguel Caparroz ; Scabini, Leonardo Felipe dos Santos ; Ribas, Lucas Correia ; Silva, Núbia Rosa da ; Beeckman, Hans; Verwaeren, Jan; Bruno, Odemir Martinez ; Baets, Bernard De
Source: Computers and Electronics in Agriculture. Unidade: IFSC
Subjects: APRENDIZADO COMPUTACIONAL, FÍSICA COMPUTACIONAL, REDES NEURAIS, PLANTAS, RECONHECIMENTO DE IMAGEM
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ABNT
ZIELINSKI, Kallil Miguel Caparroz et al. Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion. Computers and Electronics in Agriculture, v. 231, p. 109867-1-109867-12, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2024.109867. Acesso em: 15 nov. 2025.
APA
Zielinski, K. M. C., Scabini, L. F. dos S., Ribas, L. C., Silva, N. R. da, Beeckman, H., Verwaeren, J., et al. (2025). Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion. Computers and Electronics in Agriculture, 231, 109867-1-109867-12. doi:10.1016/j.compag.2024.109867
NLM
Zielinski KMC, Scabini LF dos S, Ribas LC, Silva NR da, Beeckman H, Verwaeren J, Bruno OM, Baets BD. Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion [Internet]. Computers and Electronics in Agriculture. 2025 ; 231 109867-1-109867-12.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2024.109867
Vancouver
Zielinski KMC, Scabini LF dos S, Ribas LC, Silva NR da, Beeckman H, Verwaeren J, Bruno OM, Baets BD. Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion [Internet]. Computers and Electronics in Agriculture. 2025 ; 231 109867-1-109867-12.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2024.109867
Artigo de periodico
Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits (2023)
Rodrigues, Lucas de Souza; Caixeta Filho, Edmar; Sakiyama, Kenzo ; Santos, Mateus Figueiredo; Jank, Liana ; Carromeu, Camilo ; Silveira, Eloise; Matsubara, Edson Takashi ; Marcato Júnior, José ; Gonçalves, Wesley Nunes
Source: Computers and Electronics in Agriculture. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, VISÃO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS
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ABNT
RODRIGUES, Lucas de Souza et al. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits. Computers and Electronics in Agriculture, v. 211, p. 1-14, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2023.107957. Acesso em: 15 nov. 2025.
APA
Rodrigues, L. de S., Caixeta Filho, E., Sakiyama, K., Santos, M. F., Jank, L., Carromeu, C., et al. (2023). Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits. Computers and Electronics in Agriculture, 211, 1-14. doi:10.1016/j.compag.2023.107957
NLM
Rodrigues L de S, Caixeta Filho E, Sakiyama K, Santos MF, Jank L, Carromeu C, Silveira E, Matsubara ET, Marcato Júnior J, Gonçalves WN. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits [Internet]. Computers and Electronics in Agriculture. 2023 ; 211 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2023.107957
Vancouver
Rodrigues L de S, Caixeta Filho E, Sakiyama K, Santos MF, Jank L, Carromeu C, Silveira E, Matsubara ET, Marcato Júnior J, Gonçalves WN. Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits [Internet]. Computers and Electronics in Agriculture. 2023 ; 211 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2023.107957
Artigo de periodico
On supervised learning to model and predict cattle weight in precision livestock breeding (2022)
Biase, Adriele Giaretta ; Albertini, Tiago Zanett ; Mello, Rodrigo Fernandes de
Source: Computers and Electronics in Agriculture. Unidade: ICMC
Subjects: REDES NEURAIS, PROCESSOS ESTOCÁSTICOS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), SISTEMAS DINÂMICOS, PECUÁRIA DE CORTE
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ABNT
BIASE, Adriele Giaretta e ALBERTINI, Tiago Zanett e MELLO, Rodrigo Fernandes de. On supervised learning to model and predict cattle weight in precision livestock breeding. Computers and Electronics in Agriculture, v. 195, p. 1-19, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2022.106706. Acesso em: 15 nov. 2025.
APA
Biase, A. G., Albertini, T. Z., & Mello, R. F. de. (2022). On supervised learning to model and predict cattle weight in precision livestock breeding. Computers and Electronics in Agriculture, 195, 1-19. doi:10.1016/j.compag.2022.106706
NLM
Biase AG, Albertini TZ, Mello RF de. On supervised learning to model and predict cattle weight in precision livestock breeding [Internet]. Computers and Electronics in Agriculture. 2022 ; 195 1-19.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2022.106706
Vancouver
Biase AG, Albertini TZ, Mello RF de. On supervised learning to model and predict cattle weight in precision livestock breeding [Internet]. Computers and Electronics in Agriculture. 2022 ; 195 1-19.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2022.106706
Artigo de periodico
Cattle counting in the wild with geolocated aerial images in large pasture areas (2021)
Soares, Victor Hugo Andrade ; Ponti, Moacir Antonelli ; Gonçalves, Rodrigo Almeida; Campello, Ricardo José Gabrielli Barreto
Source: Computers and Electronics in Agriculture. Unidade: ICMC
Subjects: AGRICULTURA DE PRECISÃO, PECUÁRIA, RECONHECIMENTO DE IMAGEM, REDES NEURAIS, AERONAVES NÃO TRIPULADAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOARES, Victor Hugo Andrade et al. Cattle counting in the wild with geolocated aerial images in large pasture areas. Computers and Electronics in Agriculture, v. 189, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106354. Acesso em: 15 nov. 2025.
APA
Soares, V. H. A., Ponti, M. A., Gonçalves, R. A., & Campello, R. J. G. B. (2021). Cattle counting in the wild with geolocated aerial images in large pasture areas. Computers and Electronics in Agriculture, 189, 1-14. doi:10.1016/j.compag.2021.106354
NLM
Soares VHA, Ponti MA, Gonçalves RA, Campello RJGB. Cattle counting in the wild with geolocated aerial images in large pasture areas [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106354
Vancouver
Soares VHA, Ponti MA, Gonçalves RA, Campello RJGB. Cattle counting in the wild with geolocated aerial images in large pasture areas [Internet]. Computers and Electronics in Agriculture. 2021 ; 189 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106354
Artigo de periodico
A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance (2021)
Yao, Jie-Peng; Wang, Zi-Yang; Oliveira, Ricardo Ferraz de; Wang, Zhong-Yi; Huang, Lan
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: TRIGO, SALINIDADE DO SOLO, REDES NEURAIS, ANÁLISE DE SÉRIES TEMPORAIS, SOLO SALINO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
YAO, Jie-Peng et al. A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance. Computers and Electronics in Agriculture, v. 190, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106435. Acesso em: 15 nov. 2025.
APA
Yao, J. -P., Wang, Z. -Y., Oliveira, R. F. de, Wang, Z. -Y., & Huang, L. (2021). A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance. Computers and Electronics in Agriculture, 190, 1-14. doi:10.1016/j.compag.2021.106435
NLM
Yao J-P, Wang Z-Y, Oliveira RF de, Wang Z-Y, Huang L. A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance [Internet]. Computers and Electronics in Agriculture. 2021 ; 190 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106435
Vancouver
Yao J-P, Wang Z-Y, Oliveira RF de, Wang Z-Y, Huang L. A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance [Internet]. Computers and Electronics in Agriculture. 2021 ; 190 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106435
Artigo de periodico
Detection, classification, and mapping of coffee fruits during harvest with computer vision (2021)
Bazame, Helizani Couto ; Molin, José Paulo ; Althoff, Daniel; Martello, Maurício
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, CAFÉ, COLHEITA, MATURAÇÃO VEGETAL, REDES NEURAIS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BAZAME, Helizani Couto et al. Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture, v. 183, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2021.106066. Acesso em: 15 nov. 2025.
APA
Bazame, H. C., Molin, J. P., Althoff, D., & Martello, M. (2021). Detection, classification, and mapping of coffee fruits during harvest with computer vision. Computers and Electronics in Agriculture, 183, 1-11. doi:10.1016/j.compag.2021.106066
NLM
Bazame HC, Molin JP, Althoff D, Martello M. Detection, classification, and mapping of coffee fruits during harvest with computer vision [Internet]. Computers and Electronics in Agriculture. 2021 ; 183 1-11.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106066
Vancouver
Bazame HC, Molin JP, Althoff D, Martello M. Detection, classification, and mapping of coffee fruits during harvest with computer vision [Internet]. Computers and Electronics in Agriculture. 2021 ; 183 1-11.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2021.106066
Artigo de periodico
Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes (2020)
Corte, Ana Paula Dalla; Souza, Deivison Venicio; Rex, Franciel Eduardo; Sanquetta, Carlos Roberto; Mohan, Midhun ; Silva, Carlos Alberto; Zambrano, Angelica Maria Almeyda; Prata, Gabriel; Almeida, Danilo Roberti Alves de ; Trautenmüller, Jonathan William; Klauberg, Carine; Moraes, Anibal de; Sanquetta, Mateus N; Wilkinson, Ben; Broadbent, Eben North
Source: Computers and Electronics in Agriculture. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, APRENDIZADO COMPUTACIONAL, ÁRVORES FLORESTAIS, FLORESTAS, INVENTÁRIO FLORESTAL, REDES NEURAIS, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
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ABNT
CORTE, Ana Paula Dalla et al. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, v. 179, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105815. Acesso em: 15 nov. 2025.
APA
Corte, A. P. D., Souza, D. V., Rex, F. E., Sanquetta, C. R., Mohan, M., Silva, C. A., et al. (2020). Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, 1-14. doi:10.1016/j.compag.2020.105815
NLM
Corte APD, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, Zambrano AMA, Prata G, Almeida DRA de, Trautenmüller JW, Klauberg C, Moraes A de, Sanquetta MN, Wilkinson B, Broadbent EN. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes [Internet]. Computers and Electronics in Agriculture. 2020 ; 179 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105815
Vancouver
Corte APD, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, Zambrano AMA, Prata G, Almeida DRA de, Trautenmüller JW, Klauberg C, Moraes A de, Sanquetta MN, Wilkinson B, Broadbent EN. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes [Internet]. Computers and Electronics in Agriculture. 2020 ; 179 1-14.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2020.105815
Artigo de periodico
Predictive model based on artificial neural network for assessing beef cattle thermal stress using weather and physiological variables (2018)
Sousa, Rafael Vieira de; Rodrigues, Alex Vinicius da Silva; Abreu, Mariana Gomes de; Tabile, Rubens Andre; Martello, Luciane Silva
Source: Computers and Electronics in Agriculture. Unidade: FZEA
Subjects: REDES NEURAIS, AGRICULTURA DE PRECISÃO, RADIAÇÃO TÉRMICA, BEM-ESTAR DO ANIMAL, BOVINOS DE CORTE, CONFINAMENTO ANIMAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUSA, Rafael Vieira de et al. Predictive model based on artificial neural network for assessing beef cattle thermal stress using weather and physiological variables. Computers and Electronics in Agriculture, v. 144, p. 37-43, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2017.11.033. Acesso em: 15 nov. 2025.
APA
Sousa, R. V. de, Rodrigues, A. V. da S., Abreu, M. G. de, Tabile, R. A., & Martello, L. S. (2018). Predictive model based on artificial neural network for assessing beef cattle thermal stress using weather and physiological variables. Computers and Electronics in Agriculture, 144, 37-43. doi:10.1016/j.compag.2017.11.033
NLM
Sousa RV de, Rodrigues AV da S, Abreu MG de, Tabile RA, Martello LS. Predictive model based on artificial neural network for assessing beef cattle thermal stress using weather and physiological variables [Internet]. Computers and Electronics in Agriculture. 2018 ; 144 37-43.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2017.11.033
Vancouver
Sousa RV de, Rodrigues AV da S, Abreu MG de, Tabile RA, Martello LS. Predictive model based on artificial neural network for assessing beef cattle thermal stress using weather and physiological variables [Internet]. Computers and Electronics in Agriculture. 2018 ; 144 37-43.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.compag.2017.11.033

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