Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops (2017)
- Authors:
- USP affiliated authors: BRUNO, ODEMIR MARTINEZ - IFSC ; LUZ, PEDRO HENRIQUE DE CERQUEIRA - FZEA
- Unidades: IFSC; FZEA
- DOI: 10.1109/WVC.2017.00009
- Subjects: TEXTURA; AVALIAÇÃO NUTRICIONAL; MILHO
- Keywords: Nutritional assessment; Maize leaf analysis; Deep learning; Texture analysis; Transfer learning; Convolutional neural networks
- Language: Inglês
- Imprenta:
- Publisher: Institute of Electrical and Electronics Engineers - IEEE - Computer Society
- Publisher place: Piscataway
- Date published: 2017
- Source:
- Título do periódico: Proceedings
- Conference titles: Workshop of Computer Vision - WCV
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CONDORI, Rayner Harold Montes et al. Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops. 2017, Anais.. Piscataway: Institute of Electrical and Electronics Engineers - IEEE - Computer Society, 2017. Disponível em: https://doi.org/10.1109/WVC.2017.00009. Acesso em: 16 abr. 2024. -
APA
Condori, R. H. M., Bruno, O. M., Romualdo, L. M., & Luz, P. H. de C. (2017). Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops. In Proceedings. Piscataway: Institute of Electrical and Electronics Engineers - IEEE - Computer Society. doi:10.1109/WVC.2017.00009 -
NLM
Condori RHM, Bruno OM, Romualdo LM, Luz PH de C. Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops [Internet]. Proceedings. 2017 ;[citado 2024 abr. 16 ] Available from: https://doi.org/10.1109/WVC.2017.00009 -
Vancouver
Condori RHM, Bruno OM, Romualdo LM, Luz PH de C. Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops [Internet]. Proceedings. 2017 ;[citado 2024 abr. 16 ] Available from: https://doi.org/10.1109/WVC.2017.00009 - Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants
- A diagnostic tool for magnesium nutrition in maize based on image analysis of different leaf sections
- Evaluation of nutricional status of calcium in corn plants using artificial vision system
- Calcium deficiency in corn plants identified by artificial vision system
- Método de avaliação nutricional e identificação de plantas cultivadas, por meio de imagens
- Use of digital images to identify zinc levels in maize
- Identifying corn (Zea mays L.) nutritional status by artificial vision system
- Artificial vision system for the identification of sulfur nutrition of corn plants
- Evaluation of artificial vision system to diagnose potassium status in maize plants
- Evaluation of the artificial vision system to diagnose potassium status in maize plants
Informações sobre o DOI: 10.1109/WVC.2017.00009 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
