Skip to main content

Advertisement

SpringerLink
Log in

A decision tree for nitrogen application based on a low cost radiometry

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Fertilizer recommendations based on radiometry require studies to calibrate the relationships to scenario conditions, otherwise the effectiveness may be reduced. The objective of this study was to develop a decision tree to detect nitrogen deficiency with efficiency comparable to the analysis of the full spectral signature, with simplicity similar to a spectral index and valid over a wide range of development conditions and phenological stages. An agronomic trial with a dual-purpose triticale (X Triticosecale Wittmack) was used in this study having different planting densities, number of grazing events (regeneration from defoliation) and nitrogen fertilization. At different phenological stages, the spectral signatures of leaves were recorded with an ASD-FieldSpec3 spectroradiometer and the nitrogen concentrations were determined by the Kjeldahl method. Agronomic factors that affect the N concentration were identified using ANOVA; subsequently PCA was carried out on the set of spectral signatures representative of the groups formed according to nitrogen concentration. Linear regression was used to evaluate the relationship between the principal components and plant nitrogen concentration. Wavelengths with greater significance were used to construct a decision tree. The resulting decision tree defined for nitrogen using the Jenks Natural Breaks method had a success rate of 68.3 %. The best spectral index had a R 2 = 0.31 while the estimate using the full spectral signature reached a R 2 = 0.68. Although further testing is needed, this work shows the approach was able to successfully categorize nitrogen deficiency.

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

Similar content being viewed by others

Abbreviations

Adj R 2 :

Adjusted determination coefficient

ANOVA:

Analysis of variance

MSE:

Mean square error

NDVI:

Normalized difference vegetation index

PCA:

Principal component analysis

p value:

Statistical significance

R 2 :

Coefficient of determination

RMSE:

Root mean square error

SNR:

Signal to noise ratio

SWIR:

Shortwave infrared

VNIR:

Visible and near-infrared

References

  • Alaru, M., Laur, Ü., & Jaama, E. (2003). Influence of nitrogen and weather conditions on the grain quality of winter triticale (Vol. 1). Tartu, Estonia: Estonian Agricultural University.

    Google Scholar 

  • Auernhammer, H. (2001). Precision farming—the environmental challenge. Computers and Electronics in Agriculture, 30(1–3), 31–43. doi:10.1016/s0168-1699(00)00153-8.

    Article  Google Scholar 

  • Bajwa, S. G., Bajcsy, P., Groves, P., & Tian, L. F. (2004). Hyperspectral image data mining for band selection in agricultural applications. Transactions of the ASAE, 47(3), 895–907.

    Google Scholar 

  • Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359–387. doi:10.1023/B:PRAG.0000040806.39604.aa.

    Article  Google Scholar 

  • Breiman, L. (1984). Classification and regression trees (The Wadsworth statistics/probability series). Belmont, CA: Wadsworth International Group.

  • Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote sensing of environment, 76(2), 156–172. doi:10.1016/s0034-4257(00)00197-8.

    Article  Google Scholar 

  • Broge, N. H., & Mortensen, J. V. (2002). Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 81(1), 45–57. doi:10.1016/s0034-4257(01)00332-7.

    Article  Google Scholar 

  • Chen, Y. H., Jiang, J. B., Huang, W. J., & Wang, Y. Y. (2009). Comparison of principal component analysis with VI-empirical approach for estimating severity of yellow rust of winter wheat. Guang Pu Xue Yu Guang Pu Fen Xi, 29(8), 2161–2165.

    PubMed  CAS  Google Scholar 

  • Dorigo, W. A., Zurita-Milla, R., de Wit, A. J. W., Brazile, J., Singh, R., & Schaepman, M. E. (2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2), 165–193. doi:10.1016/j.jag.2006.05.003.

    Article  Google Scholar 

  • Esbensen, K. H., & Geladi, P. (2009). Principal component analysis: Concept, geometrical interpretation, mathematical background, algorithms, history, practice. In D. B. Stephen, T. Romà, & W. Beata (Eds.), Comprehensive chemometrics (pp. 211–226). Oxford: Elsevier.

    Chapter  Google Scholar 

  • Gibson, L. R., Nance, C. D., & Karlen, D. L. (2007). Winter triticale response to nitrogen fertilization when grown after corn or soybean (Vols. 1, 99). Madison, WI, USA: American Society of Agronomy.

  • Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnell, R. B., & Viau, A. A. (2003). Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture, 39(2), 67–93.

    Article  Google Scholar 

  • Jenks, G. F. (1967). The data model concept in statistical mapping. In K. Frenzel (Ed.), International yearbook of cartography (Vol. 7, p. 186). Chicago: Rand McNally & Co.

    Google Scholar 

  • Koppe, W., Li, F., Gnyp, M. L., Miao, Y., Jia, L., Chen, X., et al. (2010). Evaluating multispectral and hyperspectral satellite remote sensing data for estimating winter wheat growth parameters at regional scale in the North China plain. Photogrammetrie - Fernerkundung - Geoinformation, 2010(3), 167–178, doi:10.1127/1432-8364/2010/0047.

    Article  Google Scholar 

  • Large, E. C. (1954). Growth stages in cereals illustration of the Feekes scale. Plant Pathology, 3(4), 128–129. doi:10.1111/j.1365-3059.1954.tb00716.x.

    Article  Google Scholar 

  • Leon, C. T., Shaw, D. R., Cox, M. S., Abshire, M. J., Ward, B., Wardlaw, M. C., et al. (2003). Utility of remote sensing in predicting crop and soil characteristics. Precision Agriculture, 4(4), 359–384. doi:10.1023/a:1026387830942.

    Article  Google Scholar 

  • Li, F., Gnyp, M. L., Jia, L., Miao, Y., Yu, Z., Koppe, W., et al. (2008). Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Research, 106(1), 77–85. doi:10.1016/j.fcr.2007.11.001.

    Article  Google Scholar 

  • Li, F., Miao, Y., Hennig, S., Gnyp, M., Chen, X., Jia, L., et al. (2010). Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture, 11(4), 335–357. doi:10.1007/s11119-010-9165-6.

    Article  Google Scholar 

  • Li, Y., Zhu, Y., Dai, T., Tian, Y., & Cao, W. (2006). Quantitative relationships between leaf area index and canopy reflectance spectra of wheat. Ying Yong Sheng Tai Xue Bao, 17(8), 1443–1447.

    PubMed  Google Scholar 

  • Llorens, J., Gil, E., Llop, J., & Escolà, A. (2010). Variable rate dosing in precision viticulture: Use of electronic devices to improve application efficiency. Crop Protection, 29(3), 239–248. doi:10.1016/j.cropro.2009.12.022.

    Article  Google Scholar 

  • Marschner, H. (1995). Mineral nutrition of higher plants (2nd ed.). London: Academic Press.

    Google Scholar 

  • Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61(3), 319–346. doi:10.1016/s0034-4257(97)00045-x.

    Article  Google Scholar 

  • Ray, S. S., Jain, N., Miglani, A., Singh, J. P., Singh, A. K., Panigrahy, S., et al. (2007). Defining optimum narrow bands and bandwidths for agricultural applications. In 58th International astronautical congress 2007, Hyderabad (Vol. 4, pp. 2458–2467).

  • R-Development_Core_Team. (2008). R: A language and environment for statistical computing. Vienna: Foundation for Statistical Computing.

  • Ren, J., Chen, Z., Tang, H., & Shi, R. (2006). Regional yield estimation for winter wheat based on net primary production model. Nongye Gongcheng Xuebao, 22(5), 111–117.

    Google Scholar 

  • Robertson, M., Isbister, B., Maling, I., Oliver, Y., Wong, M., Adams, M., et al. (2007). Opportunities and constraints for managing within-field spatial variability in Western Australian grain production. Field Crops Research, 104(1–3), 60–67. doi:10.1016/j.fcr.2006.12.013.

    Article  Google Scholar 

  • Rodriguez-Moreno, F., & Llera-Cid, F. (2011a). Evaluating spectral vegetation indices for a practical estimation of nitrogen concentration in dual-purpose (forage and grain) triticale. Evaluación de índices de vegetación espectrales para la estimación de la concentración de nitrógeno en triticale de doble aptitud (forraje y grano), 9(3), 681–686. doi:10.5424/sjar/20110903-265-10.

    Google Scholar 

  • Rodriguez-Moreno, F., & Llera-Cid, F. (2011b). PCA versus ICA for the reduction of dimensions of the spectral signatures in the search of an index for the concentration of nitrogen in plant. PCA versus ICA para la reducción de dimensiones de las firmas espectrales en la búsqueda de un índice para la concentración de nitrógeno en planta, 9(4), 1168–1175. doi:10.5424/sjar/20110904-093-11.

    Google Scholar 

  • Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99. doi:10.1016/j.compag.2010.06.009.

    Article  Google Scholar 

  • Ruß, G., & Brenning, A. (2010). Data mining in precision agriculture: Management of spatial information. In E. Hüllermeier, R. Kruse, & F. Hoffmann (Eds.), Computational intelligence for knowledge-based systems design. Lecture notes in computer science (Vol. 6178, pp. 350–359). Berlin/Heidelberg: Springer.

  • Schellberg, J., Hill, M. J., Gerhards, R., Rothmund, M., & Braun, M. (2008). Precision agriculture on grassland: Applications, perspectives and constraints. European Journal of Agronomy, 29(2–3), 59–71. doi:10.1016/j.eja.2008.05.005.

    Article  Google Scholar 

  • Schroers, J. O., Gerhards, R., & Kunisch, M. (2010). Economic evaluation of precision crop protection measures. In E. C. Oerke, R. Gerhards, G. Menz, & R. A. Sikora (Eds.), Precision crop protection—the challenge and use of heterogeneity (pp. 417–426). Netherlands: Springer.

    Chapter  Google Scholar 

  • Shao, Y., Zhao, C., Bao, Y., & He, Y. (2009). Quantification of nitrogen status in rice by least squares support vector machines and reflectance spectroscopy. Food and Bioprocess Technology, 1–8. doi:10.1007/s11947-009-0267-y.

  • Taiz, L., & Zeiger, E. (1991). Plant physiology. Redwood City, CA: Benjamin/Cummings Pub. Co.

    Google Scholar 

  • Tian, Y. C., Yang, J., Yao, X., Zhu, Y., & Cao, W. X. (2009). Quantitative relationships between hyper-spectral vegetation indices and leaf area index of rice. Ying Yong Sheng Tai Xue Bao, 20(7), 1685–1690.

    PubMed  Google Scholar 

  • Tian, Y. C., Yao, X., Yang, J., Cao, W. X., Hannaway, D. B., & Zhu, Y. (2011). Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research, 120(2), 299–310. doi:10.1016/j.fcr.2010.11.002.

    Article  Google Scholar 

  • Vrindts, E., Reyniers, M., Darius, P., De Baerdemaeker, J., Gilot, M., Sadaoui, Y., et al. (2003). Analysis of soil and crop properties for precision agriculture for winter wheat. Biosystems Engineering, 85(2), 141–152. doi:10.1016/s1537-5110(03)00040-0.

    Article  Google Scholar 

  • Waheed, T., Bonnell, R. B., Prasher, S. O., & Paulet, E. (2006). Measuring performance in precision agriculture: CART—a decision tree approach. Agricultural Water Management, 84(1–2), 173–185. doi:10.1016/j.agwat.2005.12.003.

    Article  Google Scholar 

  • Wang, Y. Y., Li, G. C., Zhang, L. J., & Fan, J. L. (2010). Retrieval of leaf water content of winter wheat from canopy hyperspectral data using partial least square regression. Guang Pu Xue Yu Guang Pu Fen Xi, 30(4), 1070–1074. doi:10.3964/j.issn.1000-0593(2010)04-1070-05.

    PubMed  CAS  Google Scholar 

  • Wood, G. A., Taylor, J. C., & Godwin, R. J. (2003). Calibration methodology for mapping within-field crop variability using remote sensing. Biosystems Engineering, 84(4), 409–423. doi:10.1016/s1537-5110(02)00281-7.

    Article  Google Scholar 

  • Yao, X., Zhu, Y., Tian, Y., Feng, W., & Cao, W. (2010). Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. International Journal of Applied Earth Observation and Geoinformation, 12(2), 89–100. doi:10.1016/j.jag.2009.11.008.

    Article  Google Scholar 

  • Yoder, B. J., & Pettigrew-Crosby, R. E. (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sensing of Environment, 53(3), 199–211. doi:10.1016/0034-4257(95)00135-n.

    Article  Google Scholar 

  • Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14(6), 415–421. doi:10.1111/j.1365-3180.1974.tb01084.x.

    Article  Google Scholar 

  • Zhang, X., Shi, L., Jia, X., Seielstad, G., & Helgason, C. (2010). Zone mapping application for precision-farming: A decision support tool for variable rate application. Precision Agriculture, 11(2), 103–114. doi:10.1007/s11119-009-9130-4.

    Article  CAS  Google Scholar 

  • Zhang, L., Zhou, Z., Zhang, G., Meng, Y., Chen, B., & Wang, Y. (2012). Monitoring the leaf water content and specific leaf weight of cotton (Gossypium hirsutum L.) in saline soil using leaf spectral reflectance. European Journal of Agronomy, 41, 103–117. doi:10.1016/j.eja.2012.04.003.

    Article  CAS  Google Scholar 

  • Zhao, C., Liu, L., Wang, J., Huang, W., Song, X., Li, C., et al. (2004). Methods and application of remote sensing to forecast wheat grain quality. In Anchorage, AK, 2004. 2004 IEEE international geoscience and remote sensing symposium proceedings: Science for society: Exploring and managing a changing planet (Vol. 6, pp. 4008–4010). IGARSS.

  • Zhou, D. Q., Tian, Y. C., Yao, X., Zhu, Y., & Cao, W. X. (2008). Quantitative relationships between leaf total nitrogen concentration and canopy reflectance spectra of rice. Ying Yong Sheng Tai Xue Bao, 19(2), 337–344.

    PubMed  CAS  Google Scholar 

  • Zhu, Y., Yao, X., Tian, Y., Liu, X., & Cao, W. (2008). Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation, 10(1), 1–10. doi:10.1016/j.jag.2007.02.006.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the staff of Mendel University, Czech Republic, Soňa Valtýniová and Vojtěch; University of Extremadura, Spain, Ángel M. Felicísimo; J. S. Schepers for their help.

Author information

Authors and Affiliations

  1. Centro de Investigación “La Orden-Valdesequera”, Junta de Extremadura, Finca “La Orden”, Ctra. N-V. Km. 372, 06187, Guadajira, Badajoz, Spain

    F. Rodriguez-Moreno & F. Llera-Cid

Authors
  1. F. Rodriguez-Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Llera-Cid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Rodriguez-Moreno.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rodriguez-Moreno, F., Llera-Cid, F. A decision tree for nitrogen application based on a low cost radiometry. Precision Agric 13, 646–660 (2012). https://doi.org/10.1007/s11119-012-9272-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-012-9272-7

Keywords

Search

Navigation