The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen

Wellington Renato Mancin, Lilian Elgalise Techio Pereira, Rachel Santos Bueno Carvalho, Yeyin Shi, Wilson Manuel Castro Silupu, Adriano Rogerio Bruno Tech


This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained
from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to
nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the fi eld, three artifi cial neural networks were
evaluated according to the performance in the classifi cation of N status: Feedforward Backpropagation (FFBP), Cascade Forward
Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the
plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N
content were obtained. Samples were then classifi ed as defi cient (< 17 g N kg-1 leaf dry matter (DM), moderately defi cient (from 17.1
to 20.0 g N kg-1 DM), and suffi cient (> 20.1 g N kg-1 DM). The VIs were selected by principal component analysis and the performance
of the networks evaluated by the accuracy. The accuracy in classifi cation obtained by the networks were 88%, 86% and 79% for FFBP,
CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the fi eld. So, the
proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive
tool for defi ning the time and the amount of N fertilizer, according to the pasture demand.


Image processing. Remote sensing. HSB. Spectral signature.

Texto completo:

PDF (English)


ATA-UL-KARIM, S. et al. Comparison of different critical nitrogen dilution curves for nitrogen diagnosis in rice. Scientific Reports, v. 7, n. 1, p. 1-14, 2017.

BARESEL, J. et al. Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Computers and Electronics in Agriculture, v. 140, p. 25–33, 2017.

BHATIA, A. et al. Greenhouse gas mitigation in rice–wheat system with leaf color chart-based urea application. Environmental Monitoring and Assessment, v. 184, p. 3095–3107, 2012.

BURGOS-ARTIZZU, X et al. Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, v. 75, p. 337–346, 2011.

CAMINHA, F. et al. Estabilidade da população de perfilhos de capim-marandu sob lotação contínua e adubação nitrogenada. Área de Informação da Sede-Artigo em periódico indexado (ALICE), 2010, doi:10.1590/S0100-204X2010000200013.

CARDOSO, A. et al. Impact of the intensification of beef production in brazil on greenhouse gas emissions and land use. Agricultural Systems, v. 143, p. 86–96, 2016.

CHEN, S.; COWAN, C.F.; GRANT, P. M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, v. 2, p. 302– 309, 1991.

COSTA, J. et al. Relative chlorophyll contents in the evaluation of the nutritional status of nitrogen from xaraes palisade grass and determination of critical nitrogen sufficiency index. Acta Scientiarum. Animal Sciences, v. 37, p. 109–114, 2015.

DE OLIVEIRA, R. et al. Sustainable intensification of brazilian livestock pro-duction through optimized pasture restoration. Agricultural Systems, v. 153, p. 201–211, 2017.

EL-AZAZY, A. Inspect the potential of using leaf image analysis procedure in estimating nitrogen status in citrus leaves. Middle East Journal of Agriculture Research, v. 7, p. 1059–1071, 2018.

EMBRAPA: Sistema brasileiro de classificação de solos. Centro Nacional de Pesquisa de Solos: Rio de Janeiro, 2013.

EMBRAPA: Banco de dados climáticos do Brasil. Accessed on August 28, 2019. Retrieved from:, 2019.

GAUTAM, R.; PANIGRAHI, S., 2007. Leaf nitrogen determination of corn plant using aerial images and artificial neural networks. Canadian Biosystems Engineering, v. 49, n. 7, 2007.

GITELSON, A. et al. 2002: Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v. 80, p. 76–87, 2002.

GUIJARRO, M. et al. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, v. 75, p. 75–83, 2011.

HE, B. et al. Estimating monthly total nitrogen concentration in streams by using artificial neural network. Journal of Environmental Management, v. 92, p. 172–177, 2011.

HU, H. et al. Estimation of leaf chlorophyll content of rice using image color analysis. Canadian Journal of Remote Sensing, v. 39, p. 185–190, 2013.

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.

KAMIJI, Y. et al. Shoot biomass in wheat is the driver for nitrogen uptake under low nitrogen supply, but not under high nitrogen supply. Field Crops Research, v.165, p. 92–98, 2014.

KARCHER, D. E.; RICHARDSON, M. Quantifying turfgrass color using digital image analysis. Crop Science, v. 43, p. 943–951, 2003.

LEE, K.; LEE, B. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal of Agronomy, v. 48, p. 57–65, 2013.

LI, Y. et al. Machine learning for the prediction of l. chinensis carbon, nitrogen and phosphorus contents and understanding of mech-anisms underlying grassland degradation. Journal of Environmental Management, v. 192, p. 116–123, 2017.

MATA-DONJUAN, G. et al. Use of improved hue, luminance and saturation (ihls) color space in the estimation of nitrogen on tomato seedlings (lycopersicon esculentum). Scientific Research and Essays, v. 7, p. 2343–2349, 2012.

MAZZETTO, A. et al. Improved pasture and herd management to reduce greenhouse gas emissions from a brazilian beef production system. Livestock Science, v. 175, p. 101–112, 2015.

MOHAN, P.; GUPTA, S. Intelligent image analysis for retrieval of leaf chlorophyll content of rice from digital images of smartphone under natural light. Photosynthetica, v. 57, p. 388–398, 2019.

NOGUEIRA, A.; SOUZA, G. Manual de laboratório: solo, água, nutrição vegetal, nutrição animal e alimentos. Embrapa Pecuária Sudeste, p. 313, 2005.

OBLITAS, J.; CASTRO, W.; MAYOR, L. Effect of different combinations of size and shape parameters in the percentage error of classification of structural elements in vegetal tissue of the pumpkin cucurbita pepo l. using probabilistic neural networks. Revista Facultad de Ingenieria Universidad de Antioquia, p. 30-37, 2016.

PAIVA, A. et al. Structural characteristics of tiller age categories of continuously stocked marandu palisade grass swards fertilized with nitrogen. Revista Brasileira de Zootecnia, v. 41, p. 24–29, 2012.

PEDREIRA, B. C.; PEDREIRA, C. G.; SILVA, S. C., 2007. Estrutura do dossel e acumulo de forragem de Brachiaria brizantha cultivar xaraes em resposta a estratégias de pastejo. Pesquisa Agropecuária Brasileira, v. 42, p. 281–287, 2007.

RAHMAT, R.; NABABAN, E. Classification of rice plant fertilizer needs based on leaf color chart using radial basis function neural network. Journal of Physics: Conference Series, p. 022037, 2018.

RIGON, J.P.G. et al. A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. Photosynthetica, v. 54, p. 559–566, 2016.

SAFA, M. et al. Modelling nitrogen content of pasture herbage using thermal images and artificial neural networks. Thermal Science and Engineering Progress, v. 11, p. 283–288, 2019.

SANTOS, P.; PRIMAVESI, O.; BERNARDI, A. Adubação de pastagens. in: Bovinocultura de corte. FEALQ: Piracicaba – Brasil, 2010, cap. 23, p. 459–472.

SCHLICHTING, A. et al. Efficiency of portable chlorophyll meters in assessing the nutritional status of wheat plants. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 19, p. 1148–1151, 2015.

SHARABIANI, V.; NAZARLOO, A.S.; TAGHINEZHAD, E. Prediction of protein content of winter wheat by canopy of near infrared spectroscopy (nirs), using partial least squares regression (plsr) and artificial neural network (ann) models. The Journal of Agricultural Science, v. 29, p. 43–51, 2019.

SHUKLA, A. et al. Calibrating the leaf color chart for nitro-gen management in different genotypes of rice and wheat in a systems perspective. Agronomy Journal, v. 96, p. 1606–1621, 2004.

SRIDEVY, S. et al. Nitrogen and potassium deficiency identification in maize by image mining, spectral and true colour response. Indian Journal of Plant Physiology, v. 23, p. 91–99, 2018.

TEWARI, V. et al. Estimation of plant nitrogen content using digital image processing. Agricultural Engineering International: CIGR Journal, v. 15, p. 78–86, 2013.

VERGARA-DIAZ, O. A novel remote sensing approach for pre-diction of maize yield under different conditions of nitrogen fertilization. Frontiers in Plant Science, v. 7, p. 666, 2016. DOI: 10.3389/fpls.2016.00666.

VESALI, F. et al. Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Computers and Electronics in Agriculture, v. 116, p. 211–220, 2015.

WANG, Y. et al. Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant Methods, v. 10, p. 36, 2014.

WANG, Y. et al. Validation of artificial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral reflectance data. International Journal of Remote Sensing, v. 30, p. 4493– 4505, 2009.

YADAV, S.; IBARAKI, Y.; GUPTA, S. Estimation of the chlorophyll content of micropropagated potato plants using rgb based image analysis. Plant Cell, Tissue and Organ Culture, v. 100, p. 183–188, 2010.

YANG, W. et al. Greenness identification based on hsv decision tree. Information Processing in Agriculture, v. 2, p. 149–160, 2015.

YASUOKA, J. et al. Canopy height and n affect herbage accumulation and the relative contribution of leaf categories to photosynthesis of grazed Brachiaria grass pastures. Grass and Forage Science, v. 73, p. 183–192, 2018.

YI, Q. et al. Evaluating the performance of pc-ann for the estimation of rice nitrogen concentration from canopy hyperspectral reflectance. International Journal of Remote Sensing, v. 31, p. 931–940, 2010.

YI, Q. X. et al. Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environmental Science & Technology, v. 41, p. 6770–6775, 2007.

ZHANG, H. et al. Prediction of crude protein content in rice grain with canopy spectral reflectance. Plant, Soil and Environment, v. 58, p. 514–520, 2012.

ZHOU, C. et al. Imaging analysis of chlorophyll fluorescence induction for monitoring plant water and nitrogen treatments. Measurement, v. 136, p. 478–486, 2019.

ZHU, Y.; HUANG, C. An improved median filtering algorithm for image noise reduction. Physics Procedia, v. 25, p. 609–616, 2012.

ZILBERMAN, A. et al. Applicability of digital color imaging for monitoring nitrogen uptake and fertilizer requirements in crops. Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 107831Y, 2018. DOI: 10.1117/12.2325765.

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