Spectral Characterization of Avocado Persea Americana Mill. Cv. Hass Using Spectrometry and Imagery from the Visible to Near-Infrared Range
Abstract
Remote sensing technologies, such as spectral imaging, have great potential for crop monitoring. Spectral systems measure the energy reflected and emitted by a surface, typically between the visible and near-infrared regions of the electromagnetic spectrum. This paper presents a spectral characterization of avocado (Persea americana Mill. cv. Hass) using spectrophotometry and spectral imaging. The study uses data from four avocado farms, which were collected in situ using spectrometers and GreenSeeker sensors and remotely using satellites such as Landsat 8 and Sentinel 2. The spectral signatures captured by the in situ and remote sensors were compared and subsequently related to vegetation indices. Spectrometry revealed differences between young and mature leaves, particularly in the 480 nm to 650 nm region of the spectrum, which showed color changes in young avocado leaves. The analysis of satellite data highlighted significant differences between Sentinel 2 and Landsat 8 spectral signatures. These differences are likely due to several factors, including collection date, preprocessing, and spatial resolution of the data. Finally, the vegetation indices derived from in situ and satellite measurements displayed different scales. For in situ data, the Normalized Difference Vegetation Index (NDVI) values were around 0.9 for the spectrometers and 0.7 for the GreenSeeker sensors. However, the NDVI values derived from satellite data were around 0.4 for Sentinel 2 and 0.3 for Landsat 8.
References
V. C. F. Gomes, G. R. Queiroz, and K. R. Ferreira, “An overview of platforms for big earth observation data management and analysis,” Remote Sens., vol. 12, no. 8, p. 1253, Apr. 2020. https://doi.org/10.3390/rs12081253
M. Rast and T. H. Painter, “Earth Observation Imaging Spectroscopy for Terrestrial Systems: An Overview of Its History, Techniques, and Applications of Its Missions,” Surv Geophys, vol. 40, pp. 303–331, Mar. 2019. https://doi.org/10.1007/s10712-019-09517-z
P. C. Pandey, N. Koutsias, G. P. Petropoulos, P. K. Srivastava, and E. Ben Dor, “Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art,” Geocarto Int., vol. 36, no. 9, pp. 957-988, Jun. 2019. https://doi.org/10.1080/10106049.2019.1629647
M. E. D. Chaves, M. C. A. Picoli, and I. D. Sanches, “Recent applications of Landsat 8/OLI and Sentinel-2/MSI for land use and land cover mapping: A systematic review,” Remote Sens., vol. 12, no. 18, p. 3062, Sep. 2020. https://doi.org/10.3390/rs12183062
G. L. Spadoni, A. Cavalli, L. Congedo, and M. Munafò, “Analysis of Normalized Difference Vegetation Index (NDVI) multi-temporal series for the production of forest cartography,” Remote Sens. Appl.: Soc. Environ., vol. 20, p. 100419, Nov. 2020. https://doi.org/10.1016/j.rsase.2020.100419
X. Zhang, J. Zhou, S. Liang, and D. Wang, “A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature,” Remote Sens. Environ., vol. 260, p. 112437, Jul. 2021. https://doi.org/10.1016/j.rse.2021.112437
M. Shimoni, R. Haelterman, and C. Perneel, “Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques,” IEEE Geosci. Remote Sens. Magazine, vol. 7, no. 2, pp. 101-117, Jun. 2019. https://doi.org/10.1109/MGRS.2019.2902525
H. Ren, Y. Zhao, W. Xiao, and Z. Hu, “A review of UAV monitoring in mining areas: Current status and future perspectives,” Int. J. Coal. Sci. Technol., vol. 6, pp. 320-333, Aug. 2019. https://doi.org/10.1007/s40789-019-00264-5
R. P. Sishodia, R. L., Ray, and S. K. Singh, “Applications of remote sensing in precision agriculture: A review,” Remote Sens., vol. 12, no. 19, p. 3136, Sep. 2020. https://doi.org/10.3390/rs12193136
L. Kumar, K. Schmidt, S. Dury, and A. Skidmore, A. “Imaging spectrometry and vegetation science,” Imaging Spectrometry, pp. 111-155, 2002. https://doi.org/10.1007/978-0-306-47578-8_5
S. L. Ustin et al, “Retrieval of foliar information about plant pigment systems from high resolution spectroscopy,” Remote Sens. Environ., vol. 113, supplement 1, pp. S67-S77, Sep. 2009. https://doi.org/10.1016/j.rse.2008.10.019
J. Xue, and B. Su, “Significant remote sensing vegetation indices: A review of developments and applications,” Journal of Sensors, vol. 2017, p. 1353691, May. 2017. https://doi.org/10.1155/2017/1353691
K. R. Thorp et al, “Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics,” Comput. Electron. Agric., vol. 118, pp. 225-236, Oct. 2015. https://doi.org/10.1016/j.compag.2015.09.005
S. Huang, L. Tang, J. P. Hupy, Y. Wang, and G. Shao, “A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing,” J. For. Res., vol. 32, pp. 1-6, May. 2020. https://doi.org/10.1007/s11676-020-01155-1
S. Jacquemoud et al., “PROSPECT+ SAIL models: A review of use for vegetation characterization,” Remote Sens. Environ., vol. 113, supplement 1, pp. S56-S66, Sep. 2009. https://doi.org/10.1016/j.rse.2008.01.026
J. Verrelst, L. Alonso, G. Camps-Valls, J. Delegido, and J. Moreno, “Retrieval of vegetation biophysical parameters using Gaussian process techniques,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 5, pp. 1832-1843, May. 2012. https://doi.org/10.1109/TGRS.2011.2168962
J. Abdulridha, R. Ehsani, and A. De Castro, “Detection and differentiation between laurel wilt disease, phytophthora disease, and salinity damage using a hyperspectral sensing technique,” Agriculture, vol. 6, no. 4, p. 56, Oct. 2016. https://doi.org/10.3390/agriculture6040056
J.J. Vega Diaz, A. P. Sandoval Aldana, and D. V. Reina Zuluaga, “Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging,” J. Sci. Food Agric., vol. 101, no. 3, pp. 897-906, Feb. 2021. https://doi.org/10.1002/jsfa.10697
S. Sankaran, R. Ehsani, S. A. Inch, and R. C. Ploetz, “Evaluation of visible-near infrared reflectance spectra of avocado leaves as a non-destructive sensing tool for detection of laurel wilt,” Plant disease, vol. 96, no. 11, pp. 1683-1689, Nov. 2012. https://doi.org/10.1094/PDIS-01-12-0030-RE
M. L. Alcaraz, T. G. Thorp, and J. I. Hormaza, “Phenological growth stages of avocado (Persea americana) according to the BBCH scale,” Scientia Horticulturae, vol. 164, pp 434-439, Dec. 2013. https://doi.org/10.1016/j.scienta.2013.09.051
M. Velez-Reyes and L. O. Jimenez, “Subset selection analysis for the reduction of hyperspectral imagery,” in IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings, Seattle, WA, USA, 1998, vol. 3, pp. 1577-1581. https://doi.org/10.1109/IGARSS.1998.691622
E. M. Barnes et al, “Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data,” In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, 2000. https://www.tucson.ars.ag.gov/unit/Publications/PDFfiles/1356.pdf
M. D. Steven, “The sensitivity of the OSAVI vegetation index to observational parameters,” Remote Sens. Environ., vol. 63, no. 1, pp. 49-60, Jan. 1998. https://doi.org/10.1016/S0034-4257(97)00114-4
C. S. T. Daughtry, C. L. Walthall, M. S. Kim, E. B. De Colstoun, and J. E McMurtrey III, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ., vol. 74, no. 2, pp. 229-239, Nov. 2000. https://doi.org/10.1016/S0034-4257(00)00113-9
J. Dash, A. Mathur, G. M. Foody, P. J. Curran, J. W. Chipman, and T. M. Lillesand, “Land cover classification using multi‐temporal MERIS vegetation indices,” Int. J. Remote Sens., vol. 28, no. 6, pp. 1137-1159, Mar. 2007. https://doi.org/10.1080/01431160600784259
M. V. Gutiérrez-Soto, E. Cadet-Piedra, W. Rodríguez-Montero, and J. M. Araya-Alfaro. “El GreenSeeker™ y el diagnóstico del estado de salud de los cultivos,” Agronomía Mesoamericana, vol. 22, no. 2, pp. 397-403, Dec. 2011. https://www.scielo.sa.cr/scielo.php?pid=S1659-13212011000200016&script=sci_arttext
M. L. Pérez-Bueno et al. “Detection of white root rot in avocado trees by remote sensing,” Plant disease, vol. 103, no. 6, pp. 1119-1125, Apr. 2019. https://doi.org/10.1094/PDIS-10-18-1778-RE
J. S. Arias Garcia, D. Pereira da Silva, A. Hurtado Salazar, R. A. Iturrieta Espinoza, and N. Ceballos-Aguirre. “Phenology of hass avocado in the Andean tropics of Caldas, Colombia,” Revista Brasileira de Fruticultura, vol. 44, no. 5, pp. 1-16, Sep. 2022. https://dx.doi.org/10.1590/0100-29452022252
J. Goudriaan and J. L. Monteith. “A mathematical function for crop growth based on light interception and leaf area expansion,” Ann. Bot. vol. 66, no. 6, pp. 695–701. Dec. 1990. https://doi.org/10.1093/oxfordjournals.aob.a088084
F. Paz-Pellat et al., “Diseño de un índice espectral de la vegetación: NDVIcp,” Agrociencia, vol. 41, no. 5, pp. 539–554. Jul. 2007. https://www.scielo.org.mx/scielo.php?pid=S1405-31952007000500539&script=sci_arttext
M. Reyes, F. Paz, M. Casiano, F. Pascual, M. I. Marín, and E. Rubiños. “Caracterización del efecto de estrés usando índices espectrales de la vegetación para la estimación de variables relacionadas con la biomasa del área,” Agrociencia vol. 45, no. 2, pp. 221-233. 2011. https://www.scielo.org.mx/scielo.php?pid=S1405-31952011000200007&script=sci_abstract&tlng=pt
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Instituto Tecnológico Metropolitano
Grant numbers P20208
