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Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato

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Abstract

An artificial neural network (ANN) was used to analyze photometric features extracted from the digitized images of leaves from in vitro-regenerated potato plants for non-invasive estimation of chlorophyll content. A MATLAB®-based, feed-forward, backpropagation-type network was developed for an input layer (three input elements), with one hidden layer (one node) and one output layer representing the predicted chlorophyll content. A significant influence of training function during optimization of ANN modeling was observed. Among the 11 training functions tested, “trainlm” was found to be the best on the basis of comparative analysis of root-mean-square error (RMSE) at zero epoch. A significant correlation between the model-predicted and Soil-Plant Analysis Development (SPAD) meter-measured relative chlorophyll contents was obtained when the mean brightness ratio (rgb) parameters were used. Compared to a red (R), green (G), and blue (B) color space model, the rgb model exhibited better performance with a significant correlation (R 2 = 0.85). Incorporation of photometric features, such as luminosity (L), blue (B)/L, and green (G)/L, with rgb failed to improve the performance of the network. The developed Intelligent image analysis (IIA) system was able to estimate in real time the chlorophyll content of in vitro-regenerated leaves for assessment of plant nutrient status during micropropagation.

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References

  • Ahmed IS, Reid JF (1996) Evaluation of color representations for maize images. J Agric Engng Res 63:185–196

    Article  Google Scholar 

  • Barber J, Horler DNH (1981) Fundamental relationships between plant spectra and geobotanical stress phenomena. Final contractor report NAS5–23738. National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD

    Google Scholar 

  • Chandra S, Bandyopadhyay R, Kumar V, Chandra R (2010) Acclimatization of tissue cultured plantlets: from lab to land. Biotechnol Lett 32:1199–1205

    Article  CAS  PubMed  Google Scholar 

  • Chappelle EW, Kim MS, McMurtrey JE (1992) Ratio analysis of reflectance spectra (RARS): an algorithm for remote estimation of the concentration of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens Environ 39:239–247

    Article  Google Scholar 

  • Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology. J Biomed Inform 35:352–359

    Article  PubMed  Google Scholar 

  • Dutta Gupta S, Ibaraki Y, Pattanayak AK (2013) Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants. Plant Biotechnol Rep 7:91–97

    Article  Google Scholar 

  • Dutta Gupta S, Ibaraki Y, Trivedi P (2014) Applications of RGB color imaging in plants. In: Dutta Gupta S, Ibaraki Y (eds) Plant image analysis: fundamentals and applications. CRC Press, Boca Raton, pp 41–62

    Google Scholar 

  • Gago J, Landin M, Gallego PP (2010a) Strengths of artificial neural networks in modeling complex plant processes. Plant Signal Behav 5:743–745

    Article  PubMed  PubMed Central  Google Scholar 

  • Gago J, Martinez-Nunez L, Landin M, Gallego PP (2010b) Artificial neural networks as an alternative to the traditional statistical methodology in plant research. J Plant Physiol 165:23–27

    Article  Google Scholar 

  • Gilmore AM, Yamamoto HY (1991) Resolution of lutein and zeaxanthin using a non-end capped, lightly carbon-loaded C-18 high-performance liquid chromatographic column. J Chromatogr A 543:137–145

    Article  CAS  Google Scholar 

  • Honda H, Takikawa N, Noguchi H, Hanai T, Kobayashi T (1997) Image analysis associated with a fuzzy neural network and estimation of shoot length of regenerated rice callus. J Ferment Bioeng 84:342–347

    Article  CAS  Google Scholar 

  • Ibaraki Y (2008) Evaluation of photosynthetic capacity in micropropagated plants by image analysis. In: Dutta Gupta S, Ibaraki Y (eds) Plant tissue culture engineering. Springer, The Netherlands, pp 15–29

    Google Scholar 

  • Ibaraki Y, Dutta Gupta S (2010) Nondestructive evaluation of the photosynthetic properties of micropropagated plantlets by imaging photochemical reflectance index under low light intensity. In Vitro Cell Dev Biol – Plant 46:530–536

    Article  CAS  Google Scholar 

  • Krogh A (2008) What are artificial neural networks? Nature Biotechnol 26:195–197

    Article  CAS  Google Scholar 

  • Lin FF, Qiu LF, Deng JS, Shi YY, Chen LS, Wang K (2010) Investigation of SPAD meter-based indices for estimating rice nitrogen status. Comput Electron Agric 71:S60–S65

    Article  Google Scholar 

  • Liu M, Liu X, Li M, Fang M, Chi W (2010) Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biosyst Eng 106:223–233

    Article  Google Scholar 

  • MacIntyre HL, Kana TM, Anning T, Geider RJ (2002) Photoacclimation of photosynthesis irradiance response curves and photosynthetic pigments in microalgae and cyanobacteria. J Phycol 38:17–38

    Article  Google Scholar 

  • Mahendra, Prasad VSS, Dutta Gupta S (2004) Trichromatic sorting of in vitro regenerated plants of gladiolus using adaptive resonance theory. Curr Sci 87:348–353

    Google Scholar 

  • Mansouri A, Fadavi A, Mortazavian SMM (2016) An artificial intelligent approach for modeling volume and fresh weight of callus—a case study cumin (Cuminum cyminum L.) J Theor Biol 397:199–205

    Article  PubMed  Google Scholar 

  • Mehrotra S, Prakash O, Mishra BN, Dwevedi B (2008) Efficiency of neural networks of prediction of in vitro culture conditions and inoculum properties for optimum productivity. Plant Cell Tissue Organ Cult 95:29–35

    Article  Google Scholar 

  • Murashige T, Skoog F (1962) A revised medium for rapid growth and bioassays with tobacco tissue culture. Physiol Plant 15:473–497

    Article  CAS  Google Scholar 

  • Nezami-Alanagh NE, Garoosi GA, Haddad R, Maleki S, Landín M, Gallego PP (2014) Design of tissue culture media for efficient Prunus rootstock micropropagation using artificial intelligence models. Plant Cell Tissue Organ Cult 117:349–359

    Article  Google Scholar 

  • Nezami-Alanagh NE, Garoosi GA, Maleki S, Landín M, Gallego PP (2017) Predicting optimum culture medium for Pistacia vera micropropagation using neural networks models. Plant Cell Tissue Organ Cult 129:19–33

    Article  CAS  Google Scholar 

  • Odabas MS, Navaratnam L, Simsek H, Padmanabhan G (2014) Quantifying impact of droughts on barley yield in North Dakota, USA using multiple linear regression and artificial neural network. Neural Network World 4(14):343–355

    Article  Google Scholar 

  • Osama K, Mishra BN, Somvanshi P (2015) Machine learning techniques in plant biology. In: Barh D, Khan MS, Davies E (eds) Plant omics: the omics of plant science. Springer, India, pp 731–754

    Google Scholar 

  • Pagola M, Ortiz R, Irigoyen I, Bustince H, Barrenechea E, Aparecio-Tejo P, Lamsfus C, Lasa B (2009) New method to assess barley nitrogen nutrition status based on image color analysis: comparison with SPAD 502. Comput Electron Agric 65:213–218

    Article  Google Scholar 

  • Prasad A, Prakash O, Mehrotra S, Khan F, Mathur AK, Mathur A (2017) Artificial neural network-based model for the prediction of optimal growth and culture conditions for maximum biomass accumulation in multiple shoot cultures of Centella asiatica. Protoplasma 254:335–341

    Article  CAS  PubMed  Google Scholar 

  • Prasad VSS, Dutta Gupta S (2008a) Applications and potentials of artificial neural networks in plant tissue culture. In: Dutta Gupta S, Ibaraki Y (eds) Plant tissue culture engineering. Springer, The Netherlands, pp 47–67

    Google Scholar 

  • Prasad VSS, Dutta Gupta S (2008b) Photometric clustering of regenerated plants of gladiolus by neural network and its biological validation. Comput Electron Agric 60:8–17

    Article  Google Scholar 

  • Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52:591–611

    Article  Google Scholar 

  • Su CH, Fu CC, Chang YC, Nair GR, Ye JL, Chu LM, Wu WT (2008) Simultaneous estimation of chlorophyll a and lipid contents in microalgae by three color analysis. Biotechnol Bioeng 99:1034–1039

    Article  CAS  PubMed  Google Scholar 

  • Uddling J, Gelang-Alfredsson J, Piiki K, Pleijel H (2007) Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth Res 91:37–46

    Article  CAS  PubMed  Google Scholar 

  • Vesali F, Omid M, Kaleita A, Mobli H (2015) Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Comput Electron Agric 116:211–220

    Article  Google Scholar 

  • Wallihan EF (1973) Portable reflectance meter for estimating chlorophyll concentration in leaves. Agron J 65:659–662

    Article  Google Scholar 

  • Wang Y, Wang D, Shi P, Omasa K (2014) Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant Methods 10:36

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yadav SP, Ibaraki Y, Dutta Gupta S (2010) Estimation of the chlorophyll content of micropropagated potato plants by RGB based image analysis. Plant Cell Tissue Organ Cult 100:183–188

    Article  CAS  Google Scholar 

  • Zeilinska A, Kepczynska E (2013) Neural modeling of plant tissue culture: a review. Biotechnologia 94:253–268

    Article  Google Scholar 

  • Zhang C, Timmis R, Hu WS (1999) A neural network based pattern recognition system for somatic embryos of Douglas fir. Plant Cell Tissue Organ Cult 56:25–35

    Article  Google Scholar 

  • Zheng CH, Sun DW, Zheng L (2006) Correlating color to moisture content of large cooked beef joints by computer vision. J Food Eng 77:858–863

    Article  Google Scholar 

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Acknowledgement

The authors wish to thank Y. Ibaraki, Department of Biological Science, Yamaguchi University, Japan, for his help in setting up the imaging system.

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Authors and Affiliations

  1. Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    S. Dutta Gupta & A. K. Pattanayak

Authors
  1. S. Dutta Gupta
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  2. A. K. Pattanayak
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Contributions

SDG designed the experiment, acquired the images, and wrote the paper. AKP did the ANN modeling and analyzed the data statistically.

Corresponding author

Correspondence to S. Dutta Gupta.

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Editor: Randall P. Niedz

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Dutta Gupta, S., Pattanayak, A.K. Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato. In Vitro Cell.Dev.Biol.-Plant 53, 520–526 (2017). https://doi.org/10.1007/s11627-017-9825-6

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  • DOI: https://doi.org/10.1007/s11627-017-9825-6

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