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: Proceedings
- Conference titles: Workshop of Computer Vision - WCV
- Este artigo NÃO possui versão em acesso aberto
-
Status: Nenhuma versão em acesso aberto identificada -
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: 14 mar. 2026. -
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 2026 mar. 14 ] 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 2026 mar. 14 ] Available from: https://doi.org/10.1109/WVC.2017.00009 - Evaluation of nutricional status of calcium in corn plants using artificial vision system
- Calcium deficiency in corn plants identified by artificial vision system
- Nutrient uptake and use of image analysis to detect nutrient deficiencies in maize subjected to the omission of NPK and Mn
- Evaluation of artificial vision system to diagnose potassium status in maize plants
- Identifying corn (Zea mays L.) nutritional status by artificial vision system
- Artificial vision system for the identification of sulfur nutrition of corn plants
- Método de avaliação nutricional e identificação de plantas cultivadas, por meio de imagens
- Evaluation of the artificial vision system to diagnose potassium status in maize plants
- Identificação de deficiência de magnésio em milho através do sistema de visão artificial
- Use of digital images to identify zinc levels in maize
Informações sobre a disponibilidade de versões do artigo em acesso aberto coletadas automaticamente via oaDOI API (Unpaywall).
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
