Apolo

Atividade

91343 - Ferramentas estatísticas e de processamento de imagens aplicadas ao monitoramento de pastagens

Período da turma:

16/03/2020 a 15/09/2020

Selecione um horário para exibir no calendário:

Descrição:

1) Dinâmica de crescimento das pastagens e absorção de nutrientes
2) Procedimentos e métodos na coleta de dados de vegetação
3) Métodos de aquisição de imagens em pastagens
4) Índices de vegetação (IV’s) com base no RGB de imagens digitais
5) Delineamento experimentais e análise de variância
6) Regressão simples e regressão múltipla
7) Análise de componentes principais
8) Processamento de imagens: linguagem de programação Python e MatLab
9) Utilização e aplicação de redes neurais artificiais para classificação, monitoramento e estimação de atributos de vegetação.

Referências bibliográficas

ABUNYEWA, A.A. et al. Grain sorghum leaf reflectance and nitrogen status. African Journal of Agricultural Research 11:825-836, 2016.
BHATIA, A. et al. Greenhouse gas mitigation in rice–wheat system with leaf color chart-based urea application. Environmental monitoring and assessment, v. 184, n. 5, p. 3095-3107, 2012.
CARDOSO, A.S. et al. Impact of the intensification of beef production in Brazil on greenhouse gas emissions land use. Agricultural Systems 143:86-96, 2016.
FRIEDMAN, J.M. et al. Assessment of leaf color chart observations for estimating maize chlorophyll content by analysis of digital photographs. Agronomy Journal, v. 108, n. 2, p. 822-829, 2016.
GITELSON, A.A. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80:76– 87, 2002.
GUIJARRO, M. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture 75:75–83, 2011.
INTARAVANNE, Y.; SUMRIDDETCHKAJORN, S. Android-based rice leaf color analyzer for estimating the needed amount of nitrogen fertilizer. Computers and Electronics in Agriculture, v. 116, p. 228-233, 2015.
KAUR, N.; SINGH, D. Android based mobile application to estimate nitrogen content in rice crop. International Journal of Computer Trends and Technology (IJCTT), v.38, n.2, 2016.
LEE, K.; LEE.B. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal of Agronomy 48:57-65, 2013.
MAZZETTO, A.M et al. Improved pasture and herd management to reduce greenhouse gas emissions from a Brazilian beef production system. Livestock Science 175:101-112, 2015.
SHARABIANI, R. et al. Multivariate analyzing and artificial neural networks for prediction of protein content in winter wheat using spectral characteristics. International Scientific Journal "Science. Business. Society" 3:153-157, 2018.
TEWARI, K.V. et al. Estimation of plant nitrogen content using digital image processing. Agricultural Engineering International 15:78-86, 2013.
WANG, Y. et al. Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant methods 10:36, 2014.
YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture 2:149-160, 2015.
YI, Q.X. et al. Evaluating the performance of PC-ANN for the estimation of rice nitrogen concentration from canopy hyperspectral reflectance. International Journal of Remote Sensing 31:931-940, 2010.

Carga Horária:

720 horas

Tipo:

Obrigatória

Vagas oferecidas:

Ministrantes:

Adriano Rogerio Bruno Tech
Lilian Elgalise Techio Pereira
Rachel Santos Bueno Carvalho

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