WATER STATUS EVALUATION OF MAIZE CULTIVARS USING AERIAL IMAGES

Authors

DOI:

https://doi.org/10.1590/1983-21252021v34n219rc

Keywords:

Zea mays L.. ARP. Remote sensing. RGB imagens.

Abstract

The objective of this study was to evaluate the water status of maize cultivars through thermal and vegetation indexes generated from multispectral aerial images obtained from an unmanned aerial vehicle (UAV), and correlate them with physiological indicators and soil water contents. The application of three water regimes based on the reference evapotranspiration (ETo) (30%, 90%, and 150% ETo) was evaluated for two maize cultivars (AG-1051 and BRS-Caatingueiro). An UAV was used to acquire thermal and multispectral images. The indexes evaluated were CWSI, CI-G, CI-RE, CIV, NDVI and OSAVI, which were correlated with gas exchange and soil moisture measures. The CWSI present correlation with physiological indicators (stomatal conductance, transpiration, and net CO2 assimilation rate) that can be used to evaluate water status of maize plants. The multispectral vegetation indexes NDVI and OSAVI can replace the CWSI thermal index in water status evaluations for maize plants.

 

Downloads

Download data is not yet available.

Author Biography

Edson Alves Bastos, Embrapa Meio-Norte, Teresina, PI

 

 

References

ALLEN, R. G. et al. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO: Rome, 1998. 300 p.

AVILA, R. G. et al. Alterações nos componentes de trocas gasosas e eficiência do fotossistema II em genótipos de milho submetidos a estresse hídrico no pré-florescimento. In: CONGRESSO NACIONAL DE MILHO E SORGO, 31, 2016, Bento Gonçalves. Anais... Bento Gonçalves: SBMS, 2016. p. 642-644.

BALUJA, J. et al. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science, 30: 511-522, 2012.

BANGARE, S. L. et al. Reviewing Otsu’s method for image thresholding. International Journal of Applied Engineering Research, 10: 21777-21783, 2015.

BASTOS, E. A. et al. Boletim agrometeorológico de 2017 para o município de Parnaíba, PI. Teresina, PI: Embrapa Meio-Norte, 2018. 37 p. (Documentos, 251).

BELLVERT, J. et al. Mapping crop water stress index in a ‘pinot-noir’ vineyard: Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture, 15: 361–376, 2014.

BELLVERT, J. et al. Vineyard irrigation scheduling based on airborne thermal imagery and water potential thresholds. Australian Journal of Grape and Wine Research, 22: 307–315, 2016.

BERGAMASCHI, H.; MATZENAUER, R. O milho e o clima. Porto Alegre, RS: Emater/RS-Ascar, 2014. 84 p.

BERNI, J. A. J. et al. Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment, 113: 2380–2388, 2009.

BIAN, J. et al. Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery. Remote Sensing, 11: 267-284, 2019.

BIANCHI, C. A. M. et al. Condutância da folha em milho cultivado em plantio direto e convencional em diferentes disponibilidades hídricas. Ciência Rural, 37: 315-322, 2007.

CARDOSO, M. J. et al. Rendimento de grãos, componentes de rendimento e eficiência de uso da água de híbridos de milho em condições climáticas contrastantes. Teresina, PI: Embrapa Meio-Norte, 2012. 23 p. (Boletim de pesquisa e desenvolvimento, 103).

CARVALHO, H. W. L. et al. Caatingueiro - Uma variedade de milho para o semiárido Nordestino. Aracaju, SE: Embrapa Tabuleiros Costeiros, 2004. 5 p. (Comunicado técnico, 29).

CASARI, R. A. C. N. et al. Using thermography to confirm genotypic variation for drought response in maize. International Journal of Molecular Sciences, 20: 2273-2295, 2019.

ESCADAFAL, R. Soil spectral properties and their relationships with environmental parameters: examples from arid regions. In: HILL, J.; MÉGIER, J. (Eds.). Imaging Spectrometry-A Tool for Environmental Observations. Dordrecht: Kluwer Academic Publishers, 1994. v. 4, cap. 5, p. 71-87.

FERREIRA, E.; CAVALCANTI, P.; NOGUEIRA, D. ExpDes: An R Package for ANOVA and Experimental Designs. Applied Mathematics, 5: 2952-2958. 2014.

FERREIRA, T.; RASBAND, W. S. ImageJ User Guide — IJ 1.46. National Institutes of Health, Bethesda, Maryland, USA, 2012. 198 p. Disponível em: <http://imagej.nih.gov/ij/ docs/guide>. Acesso em: 05 mai. 2019.

GAGO, J. et al. UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 154: 9–19, 2015.

GERHARDS, M. et al. Water stress detection in potato plants using leaf temperature, emissivity, and reflectance. International Journal of Applied Earth Observation and Geoinformation, 53: 27-39, 2016.

GHANNOUM, O. C4 photosynthesis and water stress. Annals of Botany, 103: 635-644, 2009.

GITELSON, A. A. et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30: 1248-1252, 2003.

IDSO, S. B. et al. Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24: 45-55, 1981.

LI, H. et al. Estimating crop coefficients of winter wheat based on canopy spectral vegetation indices. Transactions of Chinese Society of Agricultural Engineering, 29: 118–127, 2013.

LIU, Y. et al. Maize leaf temperature responses to drought: Thermal imaging and quantitative trait loci (QTL) mapping. Environmental and Experimental Botany, 71: 158-165, 2011.

MARTINS, J. D. Modificações morfofisiológicas em plantas de milho submetidas a déficit hídrico. 2010. 102 p. Dissertação (Mestrado em Engenharia Agrícola: Área de Concentração em Engenharia de Água e Solo) – Universidade Federal de Santa Maria, 2010.

MELO, F. B. et al. Levantamento Detalhado dos Solos da Área da Embrapa Meio-Norte / UEP de Parnaíba. Teresina, PI: Embrapa Meio-Norte, 2004. 22 p. (Documentos, 89).

MONTALVO, M. et al. Automatic detection of crop rows in maize fields with high weeds pressure. Expert System Applied, 39: 11889–11897, 2012.

OTEGUI, M. E.; ANDRADE, F. H.; SUERO, E. E. Growth, water use, and kernel abortion of maize subjected to drought at silking. Field Crop Research, 40: 87–94, 1995.

OTSU, N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man. and Cybernetics, 9: 62–66, 1979.

PADHI, J.; MISRA, R. K.; PAYERO, J. O. Estimation of soil water deficit in an irrigated cotton field with infrared thermography. Field Crops Research, 126: 45–55, 2012.

QGIS Development Team, 2016. QGIS 2.18. Geographic Information System User Guide. Open Source Geospatial Foundation Project. Disponível em: <https://docs.qgis.org/ 2.18/pdf/pt_BR/>. Acesso em: 01 nov. 2016.

RIBOLDI, L. B.; OLIVEIRA, R. F.; ANGELOCCI, L. R. Leaf turgor pressure in maize plants under water stress. Australian Journal of Crop Science, 10: 878-886, 2016.

ROMANO, G. et al. Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Computers and Electronics in Agriculture, 79: 67–74, 2011.

RONDEAUX, G.; STEVEN, M.; BARET, F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55: 95-107, 1996.

SABAGH, A. E.; BARUTÇULAR, C.; ISLAM, M. S. Relationships between stomatal conductance and yield under deficit irrigation in maize (Zea mays L.). Journal of Experimental Biology and Agricultural Sciences, 5: 14-21, 2017.

SANTOS, A. L. F. et al. Eficiência fotossintética e produtiva de milho safrinha em função de épocas de semeadura e populações de plantas. Journal of Neotropical Agriculture, 5: 52-60, 2018.

SOUSA, R. S. et al. Desempenho produtivo de genótipos de milho sob déficit hídrico. Revista Brasileira de Milho e Sorgo, 14: 49-60, 2015.

TAGHVAEIAN, S.; CHÁVEZ, J. L.; HANSEN, N. C. Infrared thermometry to estimate crop water stress index and water use of irrigated maize in Northeastern Colorado. Remote Sensing, 4:3619-3637, 2012.

TAIZ, L. et al. Fisiologia Vegetal. 6. ed. Porto Alegre, RS: Artmed, 2017. 918 p.

TORRES-SANCHEZ, J.; LOPEZ-GRANADOS, F.; PEÑA, J. M. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114: 43-52, 2015.

USAMENTIAGA, R. et al. Infrared thermography for temperature measurement and non-destructive testing. Sensors, 14: 12305–12348, 2014.

VINCINI, M.; FRAZZI, E.; D’ALESSIO, P. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture, 9: 303–319, 2008.

ZHANG, L. et al. Mapping maize water stress based on UAV multispectral remote sensing. Remote Sensing, 11: 605-629, 2019.

ZIA, S. et al. Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. Journal of Agronomy and Crop Science, 199: 75-84, 2013.

Downloads

Published

10-05-2021

Issue

Section

Agricultural Engineering