Assessment of maize hybrid water status using aerial images from an unmanned aerial vehicle

Authors

DOI:

https://doi.org/10.1590/1983-21252024v3711701rc

Keywords:

Drone. Cultivar. Water stress. Vegetation index. Remote sensing.

Abstract

The objective of this work was to evaluate the potential of vegetation indices (VIs), obtained using aerial images from an unmanned aerial vehicle (UAV), for assessing water status of maize hybrids subjected to different water regimes under the soil and climate conditions of Teresina, Piauí, Brazil. Evaluations were carried out considering the application of five water regimes (WR) based on the crop evapotranspiration (ETc) (40%, 60%, 80%, 100%, and 120% of ETc) for three maize hybrids: BRS 3046 (conventional triple hybrid), BRS 2022 (conventional double hybrid), Status VIP3 (transgenic simple hybrid). A randomized block experimental design with four replications was used, in a 5×3 split-plot arrangement, consisting of WRs in the plots and maize hybrids in the subplots. A UAV was used for acquiring multispectral images. Eighteen VIs were evaluated and correlated with stomatal conductance (gs), leaf relative water content (RWC), and grain yield (GY). The VIs TCARI-RE and NDVI presented correlation with gs, whereas MNGRD and GCI presented correlation with RWC; therefore, they were considered promising for assessing the water status of maize plants. NDVI and WDRVI presented correlations with GY. Maps of NDVI, MNGRV, and WDRVI showed spatial correlation with gs, RWC, and GY measurements, respectively, in response to the applied WRs, denoting potential for assessing the water status of maize plants using aerial images from UAV.

Downloads

Download data is not yet available.

References

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

ANDRADE JUNIOR, A. S. et al. Water status evaluation of maize cultivars using aerial images. Revista Caatinga, 34: 432-442, 2021.

BALLESTER, C. et al. Monitoring the effects of water stress in cotton using the green red vegetation index and red edge ratio. Remote Sensing, 11: 1-21, 2019.

BAJULA, 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.

BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39: 79-87, 2015.

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

BLACKBURN, G. A. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66: 273-285, 1998.

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

DAUGHTRY, C. S. T. et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74: 229-239, 2000.

DOORENBOS, J.; KASSAM, A. H. Efeito da água no rendimento das culturas. Campina Grande, PB: UFPB, 1994. 306 p. (Estudos FAO: Irrigação e Drenagem, 33).

ELMETWALLI, A. H.; TYLER, A. N. Estimation of maize properties and differentiating moisture and nitrogen deficiency stress via ground–Based remotely sensed data. Agricultural Water Management, 242: 1-7, 2020.

ELSAYED, S.; RISCHBECK, P.; SCHMIDHALTER, U. Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought stressed barley cultivars. Field Crops Research, 177: 148-160, 2015.

FERREIRA, E. B.; CAVALCANTI, P. P.; NOGUEIRA, D. A. ExpDes: an R package for ANOVA and experimental designs. Applied Mathematics, 5: 2952-2958, 2014.

GITELSON, A. A. et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: 1-4, 2005.

GITELSON, A. A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161: 165-173, 2004.

HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81: 416-426, 2002.

HAMUDA, E.; GLAVIN, M.; JONES, E. A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125: 184-199, 2016.

HASSAN, M. A. et al. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sensing, 10: 809-828, 2018.

HOYOS‐VILLEGAS, V.; FRITSCHI, F. B. Relationships among vegetation indices derived from aerial photographs and soybean growth and yield. Crop Science, 53: 2631-2642, 2013.

HOGAN, S. et al. Unmanned aerial systems for agriculture and natural resources. California Agriculture, 71: 5-14, 2017.

HUNT, E. R.; DAUGHTRY, C. S. T. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?. International Journal of Remote Sensing, 39: 5345-5376, 2018.

IHUOMA, S. O.; MADRAMOOTOO, C. A. Recent advances in crop water stress detection. Computers and Electronics in Agriculture, 141: 267-275, 2017.

INMET - Instituto Nacional de Meteorologia. Históricos de dados Meteorológicos. Disponível em: <https://portal.inmet.gov.br/dadoshistoricos>. Acesso em: 30 nov. 2020.

JONES, H. G.; VAUGHAN, R. A. Remote sensing of vegetation: principles, techniques, and applications. Oxford University Press, USA, 2010. 384 p.

LI, L. et al. Evaluating the crop water stress index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain. Agricultural Water Management, 97: 1146-1155, 2010.

MEDEIROS, M. R.; CAVALCANTI, E. P.; DUARTE, J. F. M. Classificação climática de Köppen para o estado do Piauí-Brasil. Revista Equador, 9: 82-99, 2020.

OSAKABE, Y. et al. Response of plants to water stress. Frontiers in Plant Science, 5: 81-88, 2014.

PANIGADA, C. et al. Fluorescence, PRI and canopy temperature for water stress detection in cereal crops. International Journal of Applied Earth Observation and Geoinformation, 30: 167-178, 2014.

QGIS Development Team. QGIS User Guide, Release 2.18: QGIS Project, 2016.

ROSSINI, M. et al. Discriminating irrigated and rainfed maize with diurnal fluorescence and canopy temperature airborne maps. ISPRS International Journal of Geo-Information, 4: 626-646, 2015.

SANJEREHEI, M. M. Assessment of spectral vegetation indices for estimating vegetation cover in arid and semiarid shrublands. Range Management & Agroforest, 35: 91-100, 2014.

SANTOS, H. G. et al. Brazilian Soil Classification System. 5. ed. Brasília, DF: Embrapa, 2018.

TOFFANIN, P. OpenDroneMap: The Missing Guide. 1st ed. St Petersburg, Florida: UAV4GEO, 2019. 264 p.

TUCKER, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127-150, 1979.

ULLAH, S. et al. An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis. Science of the Total Environment, 437: 145-152, 2012.

XINGYANG, S. et al. Stomatal limitations to photosynthesis and their critical Water conditions in different growth stages of maize under water stress. Agricultural Water Management, 241: 1-12, 2020.

WANG, S.; AZZARI, G.; LOBELL, D. B. Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote Sensing of Environment, 222: 303-317, 2019.

ZHANG, L. et al. Evaluating the sensitivity of water stressed maize chlorophyll and structure based on UAV derived vegetation indices. Computers and Electronics in Agriculture, 185, 1-9, 2021.

ZHAO, S. et al. Estimating and validating wheat leaf water content with three MODIS spectral indexes: A case study in Ningxia Plain, China. Journal of Agricultural Science and Technology, 18: 387-398, 2016.

ZHONG, L.; HU, L.; ZHOU, H. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221: 430-443, 2019.

Downloads

Published

22-12-2023

Issue

Section

Scientific Article