Reflectance spectrometry applied to the analysis of nitrogen and potassium deficiency in cotton

Marcio Regys Rabelo de Oliveira, Thales Rafael Guimarães Queiroz, Adunias dos Santos Teixeira, Luís Clênio Jário Moreira, Raimundo Alípio de Oliveira Leão

Resumo


The detailed study of hyperspectral data that optimise the management of agricultural inputs can be a powerful ally in the nutrient diagnosis of plants. This study characterised variations in the reflectance factors of cotton leaves (Gossypium hirsutum L.) of the BRS 293 cultivar, submitted to different levels of N and K fertilisation. A total of 166 plants were submitted to four doses of N and K, with twenty replications and six controls. Each treatment represents one level of fertilisation: 50, 75, 100 and 125% of the recommended amount of both macronutrients at each stage of the phenological cycle. The spectroradiometer used in the laboratory was the FieldSpec Pro FR 3® with a spectral resolution of 1 nm and an operating range that extends from 350 to 2500 nm. In both treatments, PCA allowed wavelengths to be idenbtified grouped by such parameters as brightness, chlorophyll and leaf moisture. The N and K fertilisation caused significant changes in the factors, where the greatest difference between doses was seen at 790 and 1198 nm. The wavelengths between 550 and 700 nm and at 1390 and 1880 nm were, respectively, the most promising for explaining the variance in nutrient levels of N and K in cotton.

Palavras-chave


Remote sensing; Hyperspectral sensor; Mineral Deficiency; Gossypium hirsutum L.

Texto completo:

PDF

Referências


ANALYTICAL SPECTRAL DEVICES. FieldSpec 3 user manual. Boulder: ASD, 2010. (ASD Document, 600540).

CHENG, T.; RIVARD, B.; SANCHEZ-AZOFEIFA, A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment, v. 115, n. 2, p. 659-670, 2011.

CILIA, C. et al. nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing, v. 6, n. 7, p. 6549-6565, 2014.

CORTI, M. et al. Hyperspectral imaging of spinach canopy under combined water and nitrogen stress to estimate biomass, water, and nitrogen content. Biosystems Engineering, v. 158, p. 38-50, 2017.

FERREIRA, G. B.; CARVALHO, M. C. S. Adubação do algodoeiro no Cerrado: com resultados de pesquisa em Goiás e Bahia. Campina Grande: Embrapa Algodão , 2005. 67 p. (Embrapa Algodão. Documentos, 138).

GIRARD, M. C.; GIRARD, C. M. Processing of Remote Sensing Data. Lisse: Balkema, 2003.

INOUE, Y. et al. Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation. Plant, Cell & Environment, v. 39, n. 12, p. 2609-2623, 2016.

JENSEN, J. R. Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres. São José dos Campos: Parêntese, 2011. 582 p.

JIA, B. et al. Essential processing methods of hyperspectral images of agricultural and food products. Chemometrics and Intelligent Laboratory Systems, 103936, 2020.

LARA, M. A. et al. Monitoring spinach shelf-life with hyperspectral image through packaging films. Journal of Food Engineering 119, no. 2 (2013): 353-361. 2013.

LIU, J. et al. Effect of late planting and shading on cotton yield and fiber quality formation. Field Crops Research, v. 183, p. 1-13, 2015.

LIU, L. et al. A novel principal component analysis method for the reconstruction of leaf reflectance spectra and retrieval of leaf biochemical contents. Remote Sensing, v. 9, n. 11, p. 1113, 2017.

MIPHOKASAP, P. et al. Estimating canopy nitrogen concentration in sugarcane using field imaging spectroscopy. Remote Sensing, v. 4, n. 6, p. 1651-1670, 2012.

MIRZAIE, M. et al. Comparative analysis of different uni-and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. International Journal of Applied Earth Observation and Geoinformation, v. 26, p. 1-11, 2014.

MISHRA, P. et al. Close range hyperspectral imaging of plants: A review. Biosystems Engineering, 164, 49-67. 2017.

MOREIRA, L. C. J.; TEIXEIRA, A. S.; GALVÃO, L. S. Laboratory salinization of brazilian alluvial soils and the spectral effects of gypsum. Remote Sensing, v. 6, n. 4, p. 2647-2663, 2014.

MOTOMIYA, V. D. A.; MOLIN, J. P.; CHIAVEGATO, E. J. Utilização de sensor óptico ativo para detectar deficiência foliar de nitrogênio em algodoeiro. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 13, n. 2, p. 137-145, 2009.

NOVO, E, M. L. M. Sensoriamento remoto: princípios e aplicações. São Paulo: Bluncher, 2008. 363 p.

PALLANT, J. SPSS Survival Manual: a step by step guide to data analysis using SPSS for Windows. 3rd ed. New York: McGraw Hill Open University Press, 2007. 353 p.

PONZONI, F. J.; DE, J. L.; GONCALVES, M. Spectral features associated with nitrogen, phosphorus, and potassium deficiencies in Eucalyptus saligna seedling leaves. International Journal of Remote Sensing, v. 20, n. 11, p. 2249-2264, 1999.

ROCHA NETO, O. C. et al. Hyperspectral remote sensing for detecting soil salinization using ProspecTIR-VS aerial imagery and sensor simulation. Remote Sensing, v. 9, n. 1, p. 42, 2017.

ROSOLEM, C. A. Acúmulo de nitrogênio, fósforo e potássio pelo algodoeiro sob irrigação cultivado em sistemas convencional e adensado. Revista Brasileira de Ciência do Solo, v. 36, n. 2, p. 457-466, 2012.

SCHLEMMERA, M. et al. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, vol. 25, p. 47-54, 2013.

SOUZA, M. F. et al. Spectral differentiation of sugarcane from weeds. Biosystems Engineering, 190, 41-46. 2020.

SUN, J. et al. Estimating rice leaf nitrogen concentration: influence of regression algorithms based on passive and active leaf reflectance. Remote Sensing, v. 9, n. 9, p. 951, 2017.

TAIZ, L.; ZEIGER, E. Fisiologia vegetal. 4. ed. Porto Alegre: Artmed, 2009. 848 p.

WANG, L.; WEI, Y. Revised normalized difference nitrogen index (NDNI) for estimating canopy nitrogen concentration in wetlands. Optik-International Journal for Light and Electron Optics, v. 127, n. 19, p. 7676-7688, 2016.

YANG, J. et al. Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra. Optics Express, v. 24, n. 17, 19354-19365, 2016.

ZHAO, D. H.; LI, J. L.; QI, J. G. Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage. Computers and Electronics in Agriculture, v. 48 n. 2, p. 155-169, 2005.




Revista Ciência Agronômica ISSN 1806-6690 (online) 0045-6888 (impresso), Site: www.ccarevista.ufc.br, e-mail: ccarev@ufc.br - Fone: (85) 3366.9702 - Expediente: 2ª a 6ª feira - de 7 às 17h.