361
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Assessing phosphorus nutritional status in maize plants using leaf-based hyperspectral measurements and multivariate analysis

ORCID Icon, , ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, & show all
Pages 2560-2580 | Received 10 Nov 2021, Accepted 03 Apr 2022, Published online: 25 Apr 2022

References

  • Adams, M. L., W. D. Philpot, and W. A. Norvell. 1999. “Yellowness Index: An Application of Spectral Second Derivatives to Estimate Chlorosis of Leaves in Stressed Vegetation.” International Journal of Remote Sensing 20 (18): 3663–3675. doi:https://doi.org/10.1080/014311699211264.
  • Al-Abbas, A. H., R. Barr, J. D. Hall, F. L. Crane, and M. F. Baumgardner. 1974. “Spectra of Normal and Nutrient-Deficient Maize Leaves.” Agronomy Journal 66 (1): 16–20. doi:https://doi.org/10.2134/agronj1974.00021962006600010005x.
  • Bedin, I., A. E. Furtini Neto, A. V. Resende, V. Faquin, A. M. Tokura, and J. Z. L. Santos. 2003. “Fertilizantes Fosfatados e Produção Da Soja Em Solos Com Diferentes Capacidades Tampão de Fosfato.” Revista Brasileira de Ciencia Do Solo 27 (4): 639–646. doi:https://doi.org/10.1590/S0100-06832003000400008.
  • Bogrekci, I., W. S. Lee, and J. D. Jordan. 2005. ”Airborne Hyperspectral Imaging for Sensing Phosphorus Concentration in the Lake Okeechobee Drainage Basin.” In Chemical and Biological Sensing VI, edited by P. J. Gardner. SPIE. doi:https://doi.org/10.1117/12.603390.
  • Brasil. Ministério da Agricultura Pecuária e Meio Ambiente - MAPA. 2018. Instrução Normativa MAPA nº 39, de 8 de agosto de 2018: Anexo I. Brasília, DF: Diário Oficial da União.
  • Carvalho, A. M., S. D. Silva, R. L. L. Leite, R. S. Pereira, A. P. Barros, L. S. Silva, and R. R. Sousa. 2017. “Avaliação De Níveis De P No Cultivo Do Milho Em Região De Transição Cerrado-Amazônia.” Global Science and Technology 10 (2): 14–24. https://rv.ifgoiano.edu.br/periodicos/index.php/gst/article/view/887.
  • Coelho, A. M. 2006. Nutrição e adubação do milho. Embrapa Milho e Sorgo. Circular técnica.
  • CONAB (Companhia Nacional de Abastecimento). (2021). “Acompanhamento da safra brasileira de grãos.” Brasília, DF, v. 8, safra 2020/21, n. 10, décimo levantamento.
  • Costa, N. V., S. D. Ferreira, and S. C. Silva. 2018. “Crescimento Inicial de Jatropa Curcas L. Sob Doses de Fósforo e Deriva de Glyphosate.” Revista Brasileira de Ciencias Agrarias/Brazilian Journal of Agricultural Sciences 13 (1): 1–7. doi:https://doi.org/10.5039/agraria.v13i1a5506.
  • Coutinho, E. L. M., W. Natale, A. S. Villa Nova, and D. S. X. Sitta. 1991. “Eficiência agronômica de fertilizantes fosfatados para a cultura da soja.” Pesquisa Agropecuária Brasileira 26 (9): 1393–1399.
  • Crusiol, L. G. T., M. R. Nanni, R. H. Furlanetto, R. N. R. Sibaldelli, E. Cezar, L. Sun, J. S. S. Foloni, L. M. Mertz-Henning, A. L. Nepomuceno, N. Neumaier, and J. R. B. Farias. 2021. ”Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance.” Remote Sensing 13 (2): 172. doi:https://doi.org/10.3390/rs13020172.
  • Cruz, C. V., D. M. Fernandes, M. A. Grohskopf, and I. V. Cruz. 2018. “Adubação Do Milho Com Superfosfato Triplo Em Latossolo Vermelho Sob Efeito Residual de Fontes Alternativas de Fósforo.” Revista de Ciências Agroveterinárias 17 (2): 166–173. doi:https://doi.org/10.5965/223811711722018166.
  • El-Hendawy, S., N. Al-Suhaibani, S. Elsayed, Y. Refay, M. Alotaibi, Y. H. Dewir, W. Hassan, and U. Schmidhalter. 2019. “Combining Biophysical Parameters, Spectral Indices and Multivariate Hyperspectral Models for Estimating Yield and Water Productivity of Spring Wheat Across Different Agronomic Practices.” PloS One 14 (3): e0212294. doi:https://doi.org/10.1371/journal.pone.0225294.
  • Falcioni, R., T. Moriwaki, C. M. Bonato, L. A. de Souza, M. R. Nanni, and W. C. Antunes. 2017. “Distinct Growth Light and Gibberellin Regimes Alter Leaf Anatomy and Reveal Their Influence on Leaf Optical Properties.” Environmental and Experimental Botany 140: 86–95. doi:https://doi.org/10.1016/j.envexpbot.2017.06.001.
  • Faquin, V. 2005. Nutrição Mineral de Plantas, 186. Lavras: UFLA/FAEPE.
  • Freire, M. L. F. 2004. “Alterações espectrais, agronômicas, de desenvolvimento e crescimento do amendoim causadas por doses de cálcio e fósforo em condições de casa-de-vegetação.” Tese (Doutorado em Recursos Naturais), Universidade Federal de Campina Grande (UECG), Campina Grande, 207.
  • Furlanetto, R. H. 2018. Sensores multi e hiperespectrais na identificação e quantificação da deficiência de potássio na cultura do milho (Zea mays).” Dissertação (Mestrado em Agronomia). Universidade Estadual de Maringá (UEM), Maringá, 133
  • Furlanetto, R. H., M. R. Nanni, M. S. Mizuno, L. G. T. Crusiol, and C. R. da Silva. 2021. “Identification and Classification of Asian Soybean Rust Using Leaf-Based Hyperspectral Reflectance.” International Journal of Remote Sensing 42 (11): 4177–4198. doi:https://doi.org/10.1080/01431161.2021.1890855.
  • Furlanetto, R. H., T. Moriwaki, R. Falcioni, M. Pattaro, A. Vollmann, A. C. S. Junior, W. C. Antunes, and M. R. Nanni. 2020. “Hyperspectral Reflectance Imaging to Classify Lettuce Varieties by Optimum Selected Wavelengths and Linear Discriminant Analysis.” Remote Sensing Applications Society and Environment 20: 100400. doi:https://doi.org/10.1016/j.rsase.2020.100400.
  • Gates, D. M., H. J. Keegan, J. C. Schleter, and V. R. Weidner. 1965. “Spectral Properties of Plants.” Applied Optics 4 (1): 11. doi:https://doi.org/10.1364/AO.4.000011.
  • Gómez-Casero, M. T., F. López-Granados, J. M. Peña-Barragán, M. Jurado-Expósito, L. García-Torres, and R. Fernández-Escobar. 2007. “Assessing Nitrogen and Potassium Deficiencies in Olive Orchards Through Discriminant Analysis of Hyperspectral Data.” Journal of the American Society for Horticultural Science 132 (5): 611–618. doi:https://doi.org/10.21273/JASHS.132.5.611.
  • Gracia-Romero, A., S. C. Kefauver, O. Vergara-Díaz, M. A. Zaman-Allah, B. M. Prasanna, J. E. Cairns, and J. L. Araus. 2017. “Comparative Performance of Ground vs. Aerially Assessed RGB and Multispectral Indices for Early-Growth Evaluation of Maize Performance Under Phosphorus Fertilization.” Frontiers in Plant Science 8. doi:https://doi.org/10.3389/fpls.2017.02004.
  • Guareschi, R. F., P. R. Gazolla, E. L. Souchie, and A. C. da Rocha. 2008. “Adubação Fosfatada e Potássica Na Semeadura e a Lanço Antecipada Na Cultura Da Soja Cultivada Em Solo de Cerrado.” Semina: Ciencias Agrarias 29 (4): 769. doi:https://doi.org/10.5433/1679-0359.2008v29n4p769.
  • Hagin, J. and J. Berkovits. 1961. “Efficiency of Phosphatic Fertilizers of Varying Water-Solubility.” Canadian Journal of Soil Science 41 (1): 68–80. doi:https://doi.org/10.4141/cjss61-009.
  • Hatfield, J. L., A. A. Gitelson, J. S. Schepers, and C. L. Walthall. 2008. “Application of Spectral Remote Sensing for Agronomic Decisions.” Agronomy Journal 100 (S3): S-117–S-131. doi: https://doi.org/10.2134/agronj2006.0370c.
  • Jacob, J. and D. W. Lawlor. 1991. “Stomatal and Mesophyll Limitations of Photosynthesis in Phosphate Deficient Sunflower, Maize and Wheat Plants.” Journal of Experimental Botany 42 (8): 1003–1011. doi:https://doi.org/10.1093/jxb/42.8.1003.
  • Jia, B., W. Wang, X. Ni, K. C. Lawrence, H. Zhuang, S.-C. Yoon, and Z. Gao. 2020. “Essential Processing Methods of Hyperspectral Images of Agricultural and Food Products.” Chemometrics and Intelligent Laboratory Systems: An International Journal Sponsored by the Chemometrics Society 198: 103936. doi:https://doi.org/10.1016/j.chemolab.2020.103936.
  • Junges, A. H., M. A. K. Almança, T. V. M. Fajardo, and J. R. Ducati. 2020. “Leaf Hyperspectral Reflectance as a Potential Tool to Detect Diseases Associated with Vineyard Decline.” Tropical Plant Pathology 45 (5): 522–533. doi:https://doi.org/10.1007/s40858-020-00387-0.
  • Karimi, Y., S. O. Prasher, H. McNairn, R. B. Bonnell, P. Dutilleul, and P. K. Goel. 2005. “Classification Accuracy of Discriminant Analysis, Artificial Neural Networks, and Decision Trees for Weed and Nitrogen Stress Detection in Corn.” Transactions of the ASAE: American Society of Agricultural Engineers 48 (3): 1261–1268. doi:https://doi.org/10.13031/2013.18490.
  • Knipling, E. B. 1970. “Physical and Physiological Basis for the Reflectance of Visible and Near-Infrared Radiation from Vegetation.” Remote Sensing of Environment 1 (3): 155–159. doi:https://doi.org/10.1016/S0034-4257(70)80021-9.
  • Kohavi, R. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Ijcai 14 (2): 1137–1145.
  • Li, L., S. Wang, T. Ren, Q. Wei, J. Ming, J. Li, X. Li, R. Cong, and J. Lu. 2018. “Ability of Models with Effective Wavelengths to Monitor Nitrogen and Phosphorus Status of Winter Oilseed Rape Leaves Using in situ Canopy Spectroscopy.” Field Crops Research 215: 173–186. doi:https://doi.org/10.1016/j.fcr.2017.10.018.
  • Mahajan, G. R., R. N. Pandey, R. N. Sahoo, V. K. Gupta, S. C. Datta, and D. Kumar. 2017. “Monitoring Nitrogen, Phosphorus and Sulphur in Hybrid Rice (Oryza Sativa L.) Using Hyperspectral Remote Sensing.” Precision Agriculture 18 (5): 736–761. doi:https://doi.org/10.1007/s11119-016-9485-2.
  • Mahajan, G. R., R. N. Sahoo, R. N. Pandey, V. K. Gupta, and D. Kumar. 2014. “Using Hyperspectral Remote Sensing Techniques to Monitor Nitrogen, Phosphorus, Sulphur and Potassium in Wheat (Triticum Aestivum L.).” Precision Agriculture 15 (5): 499–522. doi:https://doi.org/10.1007/s11119-014-9348-7.
  • Marschner, P. 2012. Marschner’s Mineral Nutrition of Higher Plants, 649. 3 ed. Austrália: School of Agriculture, Food and Wine - The University of Adelaide.
  • Martínez-Martínez, V., J. Gomez-Gil, M. L. Machado, and F. A. C. Pinto. 2018. “Leaf and Canopy Reflectance Spectrometry Applied to the Estimation of Angular Leaf Spot Disease Severity of Common Bean Crops.” PloS One 13 (4): e0196072. doi:https://doi.org/10.1371/journal.pone.0196072.
  • Muñoz-Huerta, R., R. G. Guevara-Gonzalez, L. M. Contreras-Medina, I. Torres-Pacheco, J. Prado-Olivarez, and R. V. Ocampo-Velazquez. 2013. “A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances.” Sensors (Basel, Switzerland) 13 (8): 10823–10843. doi:https://doi.org/10.3390/s130810823.
  • Nguyen, H. T. and B.-W. Lee. 2006. “Assessment of Rice Leaf Growth and Nitrogen Status by Hyperspectral Canopy Reflectance and Partial Least Square Regression.” European Journal of Agronomy: The Journal of the European Society for Agronomy 24 (4): 349–356. doi:https://doi.org/10.1016/j.eja.2006.01.001.
  • Nogueira, A. P. O., T. Sediyama, C. D. Cruz, M. S. Reis, D. G. Pereira, and M. Jangarelli. 2008. “Novas Características Para Diferenciação de Cultivares de Soja Pela Análise Discriminante.” Ciencia Rural 38 (9): 2427–2433. doi:https://doi.org/10.1590/S0103-84782008005000025.
  • Oliveira, M. R., T. R. G. Queiroz, A. D. S. Teixeira, L. C. J. Moreira, and R. A. D. O. Leão. 2020. “Reflectance Spectrometry Applied to the Analysis of Nitrogen and Potassium Deficiency in Cotton.” Ciencia Agronomica 51 (4): e20196705. doi:https://doi.org/10.5935/1806-6690.20200074.
  • Oliveira, S. B., G. Caione, M. F. Camargo, A. N. B. Oliveira, and L. Santana. 2012. “Fontes de fósforo No Estabelecimento e Produtividade de Forrageiras Na Região de Alta Floresta – MT.” Global Science and Technology 5 (1): 1–10.
  • Osborne, S. L., J. S. Schepers, D. D. Francis, and M. R. Schlemmer. 2002. “Detection of Phosphorus and Nitrogen Deficiencies in Corn Using Spectral Radiance Measurements.” Agronomy Journal 94 (6): 1215–1221. doi:https://doi.org/10.2134/agronj2002.1215.
  • Osborne, S. L., J. S. Schepers, and M. R. Schlemmer. 2004. “Detecting Nitrogen and Phosphorus Stress in Corn Using Multi-Spectral Imagery.” Communications in Soil Science and Plant Analysis 35 (3–4): 505–516. doi:https://doi.org/10.1081/CSS-120029728.
  • Pérez-Roncal, C., A. López-Maestresalas, C. Lopez-Molina, C. Jarén, J. Urrestarazu, L. G. Santesteban, and S. Arazuri. 2020. “Hyperspectral Imaging to Assess the Presence of Powdery Mildew (Erysiphe Necator) in Cv. Carignan Noir Grapevine Bunches.” Agronomy (Basel, Switzerland) 10 (1): 88. doi:https://doi.org/10.3390/agronomy10010088.
  • Perrenoud, S. 1990. Potassium and Plant Health, 363. 2 ed. Berne: International Potash Institute.
  • Pinter, P. J., Jr, J. L. Hatfield, J. S. Schepers, E. M. Barnes, M. S. Moran, C. S. T. Daughtry, and D. R. Upchurch. 2003. “Remote Sensing for Crop Management.” Photogrammetric Engineering and Remote Sensing 69 (6): 647–664. doi:https://doi.org/10.14358/PERS.69.6.647.
  • Ponzoni, F. J. 2002. Sensoriamento remoto no estudo da vegetação: diagnosticando a mata atlântica. In Curso de uso de sensoriamento remoto no estudo do meio ambiente Rudorff B. F. T., E. C. Moraes, F. J. Ponzoni, H. Camargo Júnior, J. C. Conforte, J. C. Moreira, J. C. N. Epiphanio, M. A. Moreira, M. Kampel, P. C. G. De Albuquerque, P. R. Martini, S. H. Ferreira, S. S. Tavares Júnior, and V. M. N. Santos, 27. São José dos Campos: INPE
  • Ritchie, S. W. and J. J. Hanway. 1986. How a Corn Plant Develops, 21. Vol. 48. Ames: Iowa State University of Science and Technology.
  • SAS Institute Inc. 2004. User’s Guide. SAS/STAT ® 9.1. Cary, NC: SAS Institute Inc.
  • SBCS/NEPAR (Sociedade Brasileira de Ciência do Solo/Núcleo Estadual Paraná). 2017. Manual de Adubação e Calagem Para O Estado Do Paraná, 482. Curitiba: SBCS/NEPAR.
  • Sfredo, G. J. 2008. Soja no Brasil: calagem, adubação e nutrição mineral, 145. Londrina: Embrapa soja.
  • Shi, Y., W. Huang, J. Luo, L. Huang, and X. Zhou. 2017. “Detection and Discrimination of Pests and Diseases in Winter Wheat Based on Spectral Indices and Kernel Discriminant Analysis.” Computers and Electronics in Agriculture 141: 171–180. doi:https://doi.org/10.1016/j.compag.2017.07.019.
  • Sibanda, M., O. Mutanga, M. Rouget, and J. Odindi. 2015. “Exploring the Potential Of in Situ hyperspectral Data and Multivariate Techniques in Discriminating Different Fertilizer Treatments in Grasslands.” Journal of Applied Remote Sensing 9 (1): 096033. doi:https://doi.org/10.1117/1.JRS.9.096033.
  • Silva, I. M., N. C. Schiavon, A. C. França, M. H. R. Franco, and M. M. M. Farnezi. 2019. “Respostas de Genótipos de Coffea Arabica à Aplicação de Fósforo Em Substrato Com Ácido Cítrico.” Revista de Ciências Agrárias 62. doi:https://doi.org/10.22491/rca.2019.2768.
  • Souza, D. R., C. C. Vilar, S. Y. Ushiwata, R. D. G. E. Reis, and K. C. Ribeiro. 2018. “Resposta Da Cultura Do Milho, Em Segunda Safra, à Adubação Fosfatada Em Latossolo Amarelo no Cerrado.” Revista de Ciências Agro-Ambientais 16 (1): 14–24. doi:https://doi.org/10.5327/rcaa.v16i1.1307.
  • Taiz, L. and E. Zeiger. 2013. Fisiologia Vegetal, 954. 5th ed. Porto Alegre: Artmed.
  • Thomas, S., M. Wahabzada, M. T. Kuska, U. Rascher, and A.-K. Mahlein. 2016. “Observation of Plant-Pathogen Interaction by Simultaneous Hyperspectral Imaging Reflection and Transmission Measurements.” Functional Plant Biology: FPB 44 (1): 23–34. doi:https://doi.org/10.1071/FP16127.
  • USDA (United States Department of Agriculture). 1999. Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys, 436. 2nd ed. Natural Resources Conservation Service. U.S. Department of Agriculture Handbook.
  • Ventimiglia, L. A., J. A. Costa, A. L. Thomas, and J. L. F. Pires. 1999. “Potencial de Rendimento Da Soja Em Razão Da Disponibilidade de Fósforo No Solo e Dos Espaçamentos.” Pesquisa Agropecuaria Brasileira 34 (2): 195–199. doi:https://doi.org/10.1590/S0100-204X1999000200007.
  • Vitorino, P. J. P., E. N. Santos, J. L. A. Rocha, R. M. O. S. Marcelino, and L. C. Santos. 2020. “Crescimento e Acúmulo de Fósforo Em Milho Sob Doses de Fosfato Radicular e Fosfito via Foliar.” Research, Society and Development 9 (5): e76953120. doi:https://doi.org/10.33448/rsd-v9i5.3120.
  • Xie, C., C. Yang, A. Hummel Jr, G. A. Johnson, and F. T. Izuno. 2018. “Spectral Reflectance Response to Nitrogen Fertilization in Field Grown Corn.” International Journal of Agricultural and Biological Engineering 11 (4): 102–109. doi:https://doi.org/10.25165/j.ijabe.20181104.2960.
  • Yang, J., L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song. 2016. “Estimating the Leaf Nitrogen Content of Paddy Rice by Using the Combined Reflectance and Laser-Induced Fluorescence Spectra.” Optics Express 24 (17): 19354. doi:https://doi.org/10.1364/OE.24.019354.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.