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Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

  • Research Article
  • Published:
Central European Journal of Biology

Abstract

Lack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

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References

  1. Kirakosyan A., Gibson D., Sirvent T., Comparative survey of Hypericum perforatumplants as sources of hypericins and hyperforin, Journal of Herbs Species and Medicinal Plants, 2003, 10, 110–122

    Google Scholar 

  2. Cirak C., Saglam B., Ayan A.K., Kevseroglu K., Morphogenetic and diurnal variation of hypericin in some Hypericum species from Turkey during the course of ontogenesis, Biochemical Systematics and Ecology, 2006, 34, 1–13

    Article  CAS  Google Scholar 

  3. Cirak C., Radusiene J., Janulis V., Ivanauskas L., Chemical constituents of some Hypericum species growing in Turkey, Journal of Plant Biology, 2007a, 50(6), 632–635

    Article  CAS  Google Scholar 

  4. Patocka J., The chemistry pharmacology and toxicology of the biologically active constituents of the herb Hypericum perforatum L., Journal of Applied Biomedicine, 2003, 1, 61–73

    CAS  Google Scholar 

  5. Cirak C., Radusiene J., Saglam B., Janulis V., Variation of bioactive substances and morphological traits in Hypericum perforatum populations from Northern Turkey, Biochemical Systematics and Ecology, 2007b, 35, 403–409

    Article  CAS  Google Scholar 

  6. Cirak C., Bertolli A., Pistelli L., Seyis F., Essential oil composition and variability of Hypericum perforatum from wild populations of northern Turkey, Pharmaceutical Biology, 2010, 48(8), 906–914

    Article  CAS  PubMed  Google Scholar 

  7. Vlietinck A.J., De Beuyne T., Apers S., Pieters L., Plant derived leading compounds for chemotherapy of human immunodeficiency virus (HIV) infection, Planta Medica, 1998, 64, 97–109

    Article  CAS  PubMed  Google Scholar 

  8. Cirak C., Radusiene J., Stanius Z., Camas N., Caliskan O., Odabas M.S., Secondary metabolites of Hypericum orientale L. growing in Turkey: variation among populations and plant parts, Acta Physiologia Plantarum, 2012, 34, 1313–1320

    CAS  Google Scholar 

  9. Cirak C., Radusiene J., Camas N., Caliskan O., Odabas M.S., Changes in the contents of main secondary metabolites in two Turkish Hypericum species during plant development, Pharmaceutical Biology, 2013, 51(3), 391–399

    Article  PubMed  Google Scholar 

  10. Shao H.B., Chu L.Y., Jaleel C.A., Water-deficiet stress-induced anatomical changes in higher plants, C. R. Biologies, 2008, 331(3), 215–225

    Article  PubMed  Google Scholar 

  11. Copeman R.H., Martin C.A., Stutz J.C., Tomato growth in response to salinity and mycorrhizal fungi from saline or non-saline soils, Hortscience, 1996, 31(3), 341–344

    Google Scholar 

  12. Yildirim E., Taylor A.G., Effect of biological treatments on growth of bean plants under salt stress, Annual Report of the Bean Improvement Cooperative, 2005, 48, 176–177

    Google Scholar 

  13. Flowers T.J., Flowers S.A., Why does salinity pose such a difficult problem for plant breeders? Agricultural Water Management, 2005, 78, 15–24

    Article  Google Scholar 

  14. Page A.L., Chang A.C., Adriano D.C., Agricultural salinity assessment stresses and management deficiencies and toxicities of trace elements, in: tanj, K.K. (Eds), Manuals and Reports on Eng. Practice, 1990, 71, 138–160

    Google Scholar 

  15. Said-Al Ahl H.A.H., Meawad A.A., Abou-Zeid E.N., Ali M.S., Response of different basil varieties to soil salinity, Int. Agrophysics, 2010, 24, 183–188

    Google Scholar 

  16. Kara B., Kara N., Effect of different salinity (NaCl) concentrations on the first development stages of root and shoot organs of wheat, Anadolu J. Agric. Sci., 2010, 25(1), 37–43

    Google Scholar 

  17. Erper I., Turkkan M., Odabas M.S., The mathematical approach to the effect of potassium bicarbonate on mycelial growth of Sclerotinia sclerotiorum and Rhizoctonia solani in vitro, Zemdirbyste=Agriculture, 2011, 98(2), 195–204

    Google Scholar 

  18. Bekat T., Erdogan M., Inal F., Genc A., Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks, Energy, 2012, 45, 882–887

    Article  Google Scholar 

  19. Yilmaz I., Kaynar O., Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils, Expert Systems with Applications, 2011, 38, 5958–5966

    Article  Google Scholar 

  20. Xing-Mei S., Ying-Tao J., Mei Y., Shao-Kun L., Ke-Rum W., Chong-Tao W., Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images, Agric. Sci. in China, 2010, 9(1), 38–45

    Article  Google Scholar 

  21. Arfken G., The Method of Steepest Descents, In: Mathematical Methods for Physicists, 3rd (ed.): Academic Press (Orlando, USA), 1985, 428–436

    Google Scholar 

  22. Wilamowski B.M., Chen Y., Malinowski A., Efficient algorithm for training neural networks with one hidden layer, In Neural Networks, IJCNN’ 99 International Joint Conference USA, 1999, 35–45

    Google Scholar 

  23. Hagan M.T., Menhaj M.B., Training feedforward networks with the marquardt algorithm, IEEE Transactions on Neural Networks, 1945, (6), 989–993

    Google Scholar 

  24. Jang J.S., ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Sys. Man. Cybern, 1993, 23(3), 665–685

    Article  Google Scholar 

  25. Yoneyama Y., Nonlinear control design based on generalized Takagi-Sugeno fuzzy systems, Journal of the Franklin Institute, 2013, 25–27

    Google Scholar 

  26. Jang J.S., Sun C.T., Mizutani E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Upper Saddle River NJ: Prentice-Hall, 1997, 5–15

    Google Scholar 

  27. Basheer I., Selection of methodology for modeling hyster esis behavior of soils using neural networks. J. Comput.-aided Civil Infrastruct. Eng, 2000, 5(6), 445–463

    Google Scholar 

  28. Paliwal M., Kumar U.A., Neural networks and statistical techniques: A review of applications, Expert systems with applications, 2009, 36, 2–17

    Article  Google Scholar 

  29. Tokar A.S., Johnson P.A., Rainfall-runoff modelling using artificial neural networks. Journal of Hydrologic Engineering, ASCE, 1999, 4(3), 232–239

    Google Scholar 

  30. Tayfur G., Guldal V., Artificial neural networks for estimating daily total suspended sediment in natural streams, Nordic Hydrology, 2006, 37, 69–79

    Google Scholar 

  31. Lenard M.J., Alam P., Madey G.R., The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision, Decision Sciences, 1995, 26(2), 209–227

    Article  Google Scholar 

  32. Reggia J.A., Neural computation in medicine. Artificial Intelligence Medicine, 1993, 5, 143–157

    Article  CAS  Google Scholar 

  33. Ottenbacher K.J., Smith P.M., Illig S.B., Linn R.T., Fiedler R.C., Granger C.V., Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke, Journal of Clinical Epidemiology, 2001, 54(11), 1159–1165

    Article  CAS  PubMed  Google Scholar 

  34. Feng C.X., Wang X., Digitizing uncertainty modeling for reverse engineering applications: Regression versus neural networks. Journal of Intelligent Manufacturing, 2002, 13(3), 189–199

    Article  Google Scholar 

  35. Yesilnacar E., Topal T., Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region, Turkey, Engineering Geology, 2005, 79(3–4), 251–266

    Article  Google Scholar 

  36. Fish K.E., Barnes J.H., Aiken M.W., Artificial neural networks: A new methodology for industrial market segmentation, Industrial Marketing Management, 1995, 24, 431–438

    Article  Google Scholar 

  37. Ainscough T.L., Aronson J.E., An empirical investigation and comparison of neural networks and regression for scanner data analysis, Journal of Retailing and Consumer Services, 1999, 6(4), 205–217

    Article  Google Scholar 

  38. Ayoubi S., Sahrawat K.L., Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran, Archives of Agronomy and Soil Science, 2011, 57(5), 549–565

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Agriculture, Department of Farm Structures and Irrigation, Ondokuz Mayis University, Samsun, Turkey

    Kadir Ersin Temizel

  2. Bafra Vocational School, Ondokuz Mayis University, Bafra, Samsun, Turkey

    Mehmet Serhat Odabas & Omer Caliskan

  3. Faculty of Engineering, Department of Computer Engineering, Ondokuz Mayis University, Samsun, Turkey

    Nurettin Senyer, Gokhan Kayhan & Erhan Ergun

  4. Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND, USA

    Sreekala Gopalapillai Bajwa

Authors
  1. Kadir Ersin Temizel
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  2. Mehmet Serhat Odabas
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  3. Nurettin Senyer
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  4. Gokhan Kayhan
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  5. Sreekala Gopalapillai Bajwa
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  6. Omer Caliskan
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  7. Erhan Ergun
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Correspondence to Kadir Ersin Temizel.

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Cite this article

Temizel, K.E., Odabas, M.S., Senyer, N. et al. Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions. cent.eur.j.biol. 9, 1226–1234 (2014). https://doi.org/10.2478/s11535-014-0356-4

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  • DOI: https://doi.org/10.2478/s11535-014-0356-4

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