Skip to main content
SpringerLink
Log in

Hyperspectral characterization and estimation models for agronomic parameters of coffee cultivars after pruning

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Spectral characterization of coffee cultivars after pruning may help predict the crop’s diverse phytotechnical behaviors. Therefore, this study aimed to differentiate coffee genotypes based on leaf reflectance following pruning and to develop models capable of estimating the agronomic parameters of vegetative growth. This study included eight coffee cultivars. The biochemical and leaf structure parameters were quantified for the spectral characterization of each genotype and adopted as the basis for the application of vegetation indices and to generate models that can estimate agronomic parameters after pruning. The cultivar Topázio MG-1190 proved to be the most efficient at absorbing electromagnetic radiation in the blue (220–510 nm), green (520–590 nm), and red (690–730 nm) spectral ranges among all other cultivars after pruning. The regression models could estimate the following vegetative growth parameters of the coffee cultivars: height using the normalized difference water index (root mean square error (RMSE) = 5.78%), crown diameter and plagiotropic branch length using the enhanced vegetation index (RMSE = 10.21% and 12.69%, respectively), the number of nodes using the chlorophyll b index (RMSE = 14.47%), chlorophyll a content using the photochemical reflectance index (RMSE = 2.39%), and leaf water potential using the leaf water vegetation index-1 (40.72%). The hyperspectral characterization of coffee cultivars with respect to the phytotechnical development of the genotypes after pruning was successfully accomplished. This tool can assist coffee growers to enhance their competitiveness and productivity through the development of management strategies such as the early detection of plant growth issues.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  • Abreu-Júnior, C. A. M., Vinhal, G. P., Xavier, L. C. M., Martins, G. D., & Vieira, B. S. (2020). Mapping nematodes in coffee crop from multispectral images obtained by remotely piloted aircraft. Journal Pathways of Geography, 21, 72–84. https://doi.org/10.14393/RCG217651255.

    Article  Google Scholar 

  • Ahamed, T., Tian, L., Zhang, Y., & Ting, K. C. (2011). A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy, 35, 2455–2469. https://doi.org/10.1016/j.biombioe.2011.02.028.

    Article  Google Scholar 

  • Almeida, T. S., Sediyama, G. C., & Alencar, L. P. (2017). Productivity estimation of irrigated coffee trees by the spectral agroecological zone method. Engineering in Agriculture Journal, 25, 1–11. https://doi.org/10.13083/reveng.v25i1.727.

    Article  Google Scholar 

  • Assis, G. A., Silva, L. R. S., Martins, W. E. R., Carvalho, F. J., & Pires, P. S. (2018). Growth and productivity of coffee trees in the Alto Paranaíba region as a function of the type of pruning debudding. Agricultural Science, 16, 9–12. https://doi.org/10.28998/rca.v16i2.4467.

    Article  Google Scholar 

  • Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66, 273–285. https://doi.org/10.1016/S0034-4257(98)00059-5.

    Article  Google Scholar 

  • Carvalho, C. H. S. (2008). Cultivars of coffee: Origin, characteristics and recommendations. EMBRAPA CAFÉ, 332 p.

  • Carvalho, A. M. X., & Mendes, F. Q. (2017). SPEED Stat: a minimalist and intuitive spreadsheet program for classical experimental statistics, in: Annual Meeting of the Brazilian Region of the International Society for Biometrics. Lavras, p. 62.

  • Chemura, A., Mutanga, O., Odindi, J., & Kutywayo, D. (2018). Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 1–11. https://doi.org/10.1016/j.isprsjprs.2018.02.004.

    Article  Google Scholar 

  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239. https://doi.org/10.1016/S0034-4257(00)00113-9.

    Article  Google Scholar 

  • Fazuoli, L. C., Braguini, M. T., Silvarolla, M. B., Gonçalves, W., Mistro, J. C., Gallo, P. B., & Guerreiro-Filho, O. (2018). IAC 125 RN - A dwarf coffee cultivar resistant to leaf rust and root-knot nematode. Crop Breeding and Applied Biotechnology, 18, 237–240. https://doi.org/10.1590/1984-70332018v18n2c35.

    Article  CAS  Google Scholar 

  • Formaggio, A. R., & Sanches, I. D. (2017). Remote sensing in agriculture. São Paulo: Oficina de Textos.

    Google Scholar 

  • Galvão, L. S., Formaggio, A. R., & Tisot, D. A. (2005). Discrimination of sugarcane varieties with hyperspectral data from the EO-1/Hyperion sensor (pp. 381–388). Goiânia: in: Simpósio Brasileiro de Sensoriamento Remoto.

    Google Scholar 

  • Galvíncio, J. D. (2017). Relation of leaf water content with real evapotranspiration and biomass in caating biome, using remote sensing data. Brazilian Journal of Physical Geography, 10, 1545–1551. https://doi.org/10.26848/rbgf.v10.5.p1545-1551.

    Article  Google Scholar 

  • Gamon, J. A., Peñuelas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic effciency. Remote Sensing of Environment, 41, 35–44. https://doi.org/10.1016/0034-4257(92)90059-S.

    Article  Google Scholar 

  • Gao, B. C. (1996). NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3.

    Article  Google Scholar 

  • Gitelson, A., & Solovchenko, A. (2018). Non-invasive quantification of foliar pigments: Possibilities and limitations of reflectance- and absorbance-based approaches. Journal of Photochemistry and Photobiology B: Biology, 178, 537–544. https://doi.org/10.1016/j.jphotobiol.2017.11.023.

    Article  CAS  PubMed  Google Scholar 

  • Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416–426. https://doi.org/10.1016/S0034-4257(02)00018-4.

    Article  Google Scholar 

  • Hanes, J. M., Liang, L., & Morisette, J. T. (2013). Land surface phenology. In H. Jonathan (Ed.), Biophysical applications of satellite remote sensing (pp. 99–125). Springer.

  • Hernández-Clemente, R., Hornero, A., Mottus, M., Penuelas, J., González-Dugo, V., Jiménez, J. C., Suárez, L., Alonso, L., & Zarco-Tejada, P. J. (2019). Early diagnosis of vegetation health from high-resolution hyper spectral and thermal imagery: Lessons learned from empirical relationships and radiative transfer modelling. Current forestry Reports, 5, 169–183. https://doi.org/10.1007/s40725-019-00096-1.

    Article  Google Scholar 

  • Jacquemoud, S. (1993). Inversion of the PROSPECT + SAIL canopy reflectance model from AVIRIS equivalent spectra: Theoretical study. Remote Sensing of Environment, 44, 281–292. https://doi.org/10.1016/0034-4257(93)90022-P.

    Article  Google Scholar 

  • Japiassú, L. B., Garcia, A. L. A., Guimarães, R. J., Padilha, L., & Carvalho, C. H. S. (2010). Pruning cycles and nitrogen fertilization in coffee crops conducted in the “zero crop” system. Coffee Science, 5, 28–37.

    Google Scholar 

  • Jiang, J., Comar, A., Burger, P., Bancal, P., Weiss, M., & Baret, F. (2018). Estimation of leaf traits from reflectance measurements: Comparison between methods based on vegetation indices and several versions of the PROSPECT model. Plant Methods, 14, 23. https://doi.org/10.1186/s13007-018-0291-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833–3845. https://doi.org/10.1016/j.rse.2008.06.006.

    Article  Google Scholar 

  • Lichtenthaler, H. K., & Wellburn, A. R. (1983). Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Biochemical Society Transactions, 11, 591–592. https://doi.org/10.1042/bst0110591.

    Article  CAS  Google Scholar 

  • Marin, D. B., Alves, M. C., Pozza, E. A., Belan, L. L., & Freitas, M. L. O. (2019). Multispectral radiometric monitoring of bacterial blight of coffee. Precision Agriculture, 20(5), 959–982. https://doi.org/10.1007/s11119-018-09623-9.

    Article  Google Scholar 

  • Martins, G. D., Galo, M. L. B. T., & Vieira, B. S. (2017). Detecting and mapping root-knot nematode infection in coffee crop u sing remote sensing measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 5395–5403.

    Article  Google Scholar 

  • Ponzoni, F. J., Shimabukuro, Y. E., & Kuplich, T. M. (2012). Remote sensing of Vegetation. São Paulo: Oficina de Textos.

    Google Scholar 

  • RNC - National Register of Cultivars. Ministry of Agriculture, Livestock and Supply (2022). Retrieved March 1, 2022, from http://sistemas.agricultura.gov.br/snpc/cultivarweb/cultivares_registradas.php.

  • Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS, in: ERTS-1 Symposium, Washington, NASA SP-351, pp. 309–317.

  • Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S., & Martonchik, J. V. (2006). Reflectance quantities in optical remote sensing - definitions and case studies. Remote Sensing of Environment, 103, 27–42. https://doi.org/10.1016/j.rse.2006.03.002.

    Article  Google Scholar 

  • Schenone, G. R., Nosetto, M. D., & Jobbágy, E. G. (2017). Combined use of thermal and spectral remote sensing to assess coffee plantations’ water use and growth. Remote Sensing, 9(3), 272.

    Google Scholar 

  • Silva, V. A., Rezende, J. C., Carvalho, A. M., Carvalho, G. R., Rezende, T. T., & Ferreira, A. D. (2016). Recuperação de cultivares de café submetidas ao esqueletamento aos quatro anos e meio de idade. Coffee Science, 11, 55–64.

    Google Scholar 

  • Tisot, D. A., Formaggio, A. R., Galvão, L. S., & Rennó, C. D. (2005). Hyperion and ETM + data in the classification of agricultural targets with different levels of spectral distinction (pp. 439–446). Goiânia: Simpósio Brasileiro de Sensoriamento Remoto.

    Google Scholar 

  • Verdin-Filho, A. C., Volpi, P. S., Ferrão, M. A. G., Fonseca, A. F. A., Alixandre, F. T., Comércio, M., & Kaulz, M. (2018). Scheduled cycle pruning in Arabica coffee - PPCA. Paper no. 242, Incaper, Vitória-ES.

  • Witham, F. H., Blaydes, D. F., & Devlin, R. M. (1971). Experiments in Plant Physiology. New York: D. Van Nostrand Company.

    Google Scholar 

  • Yudina, L., Sukhova, E., Gromova, E., Nerush, V., Vodenee, V. V., & Sukhov, V. (2020). A light-induced decrease in the photochemical reflectance index (PRI) can be used to estimate the energy-dependent component of non-photochemical quenching under heat stress and soil drought in pea, wheat, and pumpkin. Photosynthesis Research, 146, 175–187. https://doi.org/10.1007/s11120-020-00718-x.

    Article  CAS  PubMed  Google Scholar 

  • Zhao, B., Duan, A., Ata-Ul-Karim, S. T., Liu, Z., Chen, Z., Gong, Z., Zhang, J., Xiao, J., Liu, Z., Quin, A., & Ning, D. (2018). Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. European Journal of Agronomy, 93, 113–125.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Master’s Program in Agriculture and Geospatial Information, Federal University of Uberlândia, Monte Carmelo, MG, Brazil

    Renato Aurélio Severino de Menezes Freitas

  2. Civil Engineering College, Federal University of Uberlândia, Monte Carmelo, MG, Brazil

    George Deroco Martins

  3. Institute of Agricultural Sciences, Federal University of Uberlândia, Monte Carmelo, MG, Brazil

    Gleice Aparecida Assis, Marco Iony dos Santos Fernandes, Maria Olivia Queiroz Silva Soares & Bruno Eduardo Caetano Campos da Silva Pinheiro

  4. Biotechnology Institute, Federal University of Uberlândia, Monte Carmelo, MG, Brazil

    Ana Carolina Silva Siquieroli

Authors
  1. Renato Aurélio Severino de Menezes Freitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Deroco Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gleice Aparecida Assis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ana Carolina Silva Siquieroli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco Iony dos Santos Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maria Olivia Queiroz Silva Soares
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bruno Eduardo Caetano Campos da Silva Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gleice Aparecida Assis.

Ethics declarations

Conflicts of interest/competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Menezes Freitas, R.A.S., Martins, G.D., Assis, G.A. et al. Hyperspectral characterization and estimation models for agronomic parameters of coffee cultivars after pruning. Precision Agric 24, 2374–2394 (2023). https://doi.org/10.1007/s11119-023-10044-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-023-10044-6

Keywords

Search

Navigation