Skip to main content
SpringerLink
Log in

Machine Learning Techniques in Plant Biology

  • Chapter
  • First Online:
PlantOmics: The Omics of Plant Science

Abstract

There is an increasing amount of various genome-sequencing projects and advancement in generation of plant ESTs has resulted in generation of large quantities of data from different fields of plant biology in the public domain. Therefore, a need arises in the analysis of the available data and integrating them with several information of plant biology like crop improvement, nutrigenomics, biochemical engineering, etc. The biological data are mostly complex and vague, analysis of these data is difficult, and interpretation of interaction in different elements cannot be done by simple mathematical functions. Complex computing approaches like artificial intelligence are being applied to understand and interpret these data. The definition of intelligence is debatable for a long period of time; however, intelligence can be vaguely defined as the ability to learn from previous experiences and to adapt accordingly in relatively new situations. Artificial intelligence uses machine learning algorithm in which the system generates some adaptive learning approaches in order to achieve some goal of environment. Several machine learning approaches have been applied in plant biology till date. In this chapter we will discuss few machine learning approaches and their applications in plant biology.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adriaenssens V, De Baets B, Goethals P, De Pauw N (2004) Fuzzy rule-based models for decision support in ecosystem management. Sci Total Environ 319(1):1–12

    Article  CAS  PubMed  Google Scholar 

  • Agazzi OE, Kuo SS (1993) Hidden Markov model based optical character recognition in the presence of deterministic transformations. Pattern Recogn 26(12):1813–1826

    Article  Google Scholar 

  • Albiol J, Campmajó C, Casas C, Poch M (1995) Biomass estimation in plant cell cultures: a neural network approach. Biotechnol Prog 11:88–92

    Article  CAS  Google Scholar 

  • Alexander J, Mozer M (1999) Template-based procedures for neural network interpretation. Neural Netw 12:479–498

    Article  PubMed  Google Scholar 

  • Andrews R, Diederich J, Tickle AB (1995) Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl-Based Syst 8:373–389

    Article  Google Scholar 

  • Archer NP, Wang S (1993) Application of the back propagation neural network algorithm with monotonicity constraints for two‐group classification problems. Decis Sci 24:60–67

    Article  Google Scholar 

  • Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach. MIT Press, Cambridge, MA

    Google Scholar 

  • Borodovsky M, McIninch J (1993) GENMARK: parallel gene recognition for both DNA strands. Comput Chem 17:123–133

    Article  CAS  Google Scholar 

  • Borodovsky M, Rudd KE, Koonin EV (1994) Intrinsic and extrinsic approaches for detecting genes in a bacterial genome. Nucleic Acids Res 22(2):4756–4767

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Borodovsky M, Mclninch JD, Koonin EV, Rudd KE, Médigue C, Danchin A (1995) Detection of new genes in a bacterial genome using Markov models for three gene classes. Nucleic Acids Res 23:3554–3562

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Chuong BD, Serafim B (2008) What is the expectation maximization algorithm? Nat Biotechnol 26(8):897–899

    Article  Google Scholar 

  • Colbourn E (2003) Neural computing: enable intelligent formulations. Pharm Technol Suppl 16–20

    Google Scholar 

  • Coppola EA, Rana AJ, Poulton MM, Szidarovszky F, Uhl VW (2005) A neural network model for predicting aquifer water level elevations. Ground Water 43(2):231–241

    Article  CAS  PubMed  Google Scholar 

  • Coruzzi GM, Burga AR, Katari MS, Gutiérrez RA (2009) Systems biology: principles and applications in plant research. Annu Plant Rev 35:3–40

    CAS  Google Scholar 

  • Denton JW (1995) How good are neural networks for casual forecasting? J Bus Forecast Methods Syst 14(2):17–20

    Google Scholar 

  • D’haeseleer P (2005) How does gene expression clustering work? Nat Biotechnol 23:1499–1502

    Article  PubMed  Google Scholar 

  • Eichner J, Zeller G, Laubinger S, Ratsch G (2011) Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays. BMC Bioinform 12(1):5

    Article  Google Scholar 

  • Excoffier L, Slatkin M (1995) Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Biol Evol 12:921–927

    CAS  PubMed  Google Scholar 

  • Feng Y, Xue Q (2006) The serine carboxypeptidase like gene family of rice (Oryza sativa L. ssp. japonica). Funct Integr Genomics 6(1):14–24

    Article  CAS  PubMed  Google Scholar 

  • Filippi AM, Archibald R (2009) Support vector machine-based endmember extraction. IEEE Trans Geosci Remote Sens 47(3):771–791

    Article  Google Scholar 

  • Frossyniotis D, Anthopoulos Y, Kintzios S, Moschopoulou G, Yialouris C (2008) Artificial neural network selection for the detection of plant viruses. World J Agric Sci 4(1):114–120

    Google Scholar 

  • Fukuda T, Shiotani S, Aral F, Takeuchi N, Sasaki K, Kimoshita T (1991) Cell recognition by image processing. 1st report. Recognition of dead or alive plant cells by neural network. Trans Jpn Soc Mech Eng Ser 57:77–84

    Google Scholar 

  • Gago J, Landín M, Gallego P (2010a) Strengths of artificial neural networks in modeling complex plant processes. Plant Signal Behav 5(6):743–774

    Article  PubMed Central  PubMed  Google Scholar 

  • Gago J, Landín M, Gallego PP (2010b) Artificial neural networks modeling the in vitro rhizogenesis and acclimatization of Vitis vinifera L. J Plant Physiol 167:1226–1231

    Article  CAS  PubMed  Google Scholar 

  • Gago J, Martínez-Núñez L, Landín M, Gallego P (2010c) Artificial neural networks as an alternative to the traditional statistical methodology in plant research. J Plant Physiol 167:23–27

    Article  CAS  PubMed  Google Scholar 

  • Gallego PP, Gago J, Landín M (2011) Artificial neural networks technology to model and predict plant biology process. In: Suzuki K (ed) Artificial neural networks-methodological advances and biomedical applications. Intech, Rijeka, Carotia, pp 197–216

    Google Scholar 

  • Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural networks. Ecol Model 160(3):249–264

    Article  Google Scholar 

  • Glezakos TJ, Moschopoulou G, Tsiligiridis TA, Kintzios S, Yialouris CP (2010) Plant virus identification based on neural networks with evolutionary preprocessing. Comput Electron Agric 70:263–275

    Article  Google Scholar 

  • Gonzalez S (2000) Neural networks for macroeconomic forecasting: a complementary approach to linear regression models, Working paper. Department of Finance, Canada, pp 2000–2007

    Google Scholar 

  • Gu L, Guo R (2007) Genome-wide detection and analysis of alternative splicing for nucleotide binding site-leucine-rich repeats sequences in rice. J Genet Genomics 34(3):247–257

    Article  CAS  PubMed  Google Scholar 

  • Hanai T, Katayama A, Honda H, Kobayaski T (1997) Automatic fuzzy modelling for Ginjo sake brewing process using fuzzy neural network. J Chem Eng Jpn 30:94–100

    Article  CAS  Google Scholar 

  • Hayashi Y, Buckley JJ, Czogala E (1993) Fuzzy neural network with fuzzy signals and weights. Int J Intell Syst 8:527–537

    Article  Google Scholar 

  • Haykin S (2003) Neural networks: a comprehensive foundation, fourth Indian reprint. Pearson Education, Singapore

    Google Scholar 

  • Hilbert DW, Ostendorf B (2001) The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecol Model 146:311–327

    Article  Google Scholar 

  • Hill T, Marquez L, O’Connor M, Remus W (1994) Artificial neural network models for forecasting and decision making. Int J Forecast 10:5–15

    Article  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 

  • Honda H, Ito T, Yamada J, Hanai T, Matsuoka M, Kobayashi T (1999) Selection of embryogenic sugarcane callus by image analysis. J Biosci Bioeng 87(5):700–702

    Article  CAS  PubMed  Google Scholar 

  • Hua S, Sun Z (2001) A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 308(2):397–408

    Article  CAS  PubMed  Google Scholar 

  • Huang Y (2009) Advances in artificial neural networks – methodological development and application. Algorithms 2:973–1007

    Article  Google Scholar 

  • Jiménez D, Pérez-Uribe A, Satizábal H, Barreto M, Van Damme P, Marco T (2008) A survey of artificial neural network-based modeling in agroecology. In: Prasad B (ed) Soft computing applications in industry, STUDFUZZ. Springer, Berlin/Heidelberg, pp 247–269

    Google Scholar 

  • Juang B-H, Rabiner LR (1991) Hidden Markov models for speech recognition. Technometrics 33(3):251–272

    Article  Google Scholar 

  • Karim MN, Yoshida T, Rivera SL, Saucedo VM, Eikens B, Oh G-S (1997) Global and local neural network models in biotechnology: application to different cultivation processes. J Ferment Bioeng 83(1):1–11

    Article  CAS  Google Scholar 

  • Karimi Y, Prasher S, Patel R, Kim S (2006) Application of support vector machine technology for weed and nitrogen stress detection in corn. Comput Electron Agric 51(1):99–109

    Article  Google Scholar 

  • Kaul M, Hill RL, Walthall C (2005) Artificial neural networks for corn and soybean yield prediction. Agric Syst 85:1–18

    Article  Google Scholar 

  • Kaundal R, Saini R, Zhao PX (2010) Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis. Plant Physiol 154(1):36–54

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kehagias A, Panagiotou H, Maslaris N, Petridis V, Petrou L (1998) Predictive modular neural networks methods for prediction of sugar beet crop yield. In: IFAC conference on control applications and ergonomics in agriculture, Athens, pp 1–5

    Google Scholar 

  • Kell DB, Darby RM, Draper J (2001) Genomic computing. Explanatory analysis of plant expression profiling data using machine learning. Plant Physiol 126:943–951

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Korf I (2004) Gene finding in novel genomes. BMC Bioinform 5:59

    Article  Google Scholar 

  • Krenker A, Bešter J, Kos A (2011) Introduction to the artificial neural networks. In: Suzuki K (ed) Artificial neural networks-methodological advances and biomedical applications, Intech, Rijeka, Carotia, pp 3–18

    Google Scholar 

  • Krogh A, Brown M, Mian IS, Sjolander K, Haussler D (1994) Hidden Markov models in computational biology: applications to protein modeling. J Mol Biol 235(5):1501–1531

    Article  CAS  PubMed  Google Scholar 

  • Kruschke JK, Movellan JR (1991) Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks. IEEE Trans Syst Man Cybern 21(1):273–280

    Article  Google Scholar 

  • Landín M, Rowe R, York P (2009) Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations. Eur J Pharm Sci 38:325–331

    Article  PubMed  Google Scholar 

  • Lawrence CE, Reilly AA (1990) An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 7:41–51

    Article  CAS  PubMed  Google Scholar 

  • Lin M, Hu B, Chen L, Sun P, Fan Y, Wu P, Chen X (2009) Computational identification of potential molecular interactions in Arabidopsis. Plant Physiol 151(1):34–46

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Mahendra V, 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 

  • Majoros WH, Pertea M, Antonescu C, Salzberg SL (2003) GlimmerM, exonomy and unveil: three ab initio eukaryotic genefinders. Nucleic Acids Res 31(13):3601–3604

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Mandic DP, Chambers J (2001) Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley, Chichester/New York

    Book  Google Scholar 

  • Marcuzzo M, Quelhas P, Campilho A, Mendonça AM (2008a) Automatic cell segmentation from confocal microscopy images of the Arabidopsis root. ISBI 2008 5th IEEE international symposium on Biomedical Imaging: From Nano to Macro. 712–771

    Google Scholar 

  • Marcuzzo M, Quelhas P, Campilho A, Mendonça AM, Campilho A (2008b) A hybrid approach for Arabidopsis root cell image segmentation. In: Campilha A, Kamel M (eds) Image analysis and recognition. Springer Berlin Heidelberg, pp 739–749

    Google Scholar 

  • Marcuzzo M, Quelhas P, Campilho A, Maria Mendonça A, Campilho A (2009) Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging. Comput Biol Med 39:785–793

    Article  PubMed  Google Scholar 

  • Martinez PJ, Pérez RM, Plaza A, Aguilar PL, Cantero MC, Plaza J (2006) Endmember extraction algorithms from hyperspectral images. Ann Geophys 49(1):93–101

    Google Scholar 

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

    Article  Google Scholar 

  • Mehrotra S, Prakash O, Khan F, Kukreja A (2013) Efficiency of neural network-based combinatorial model predicting optimal culture conditions for maximum biomass yields in hairy root cultures. Plant Cell Rep 32:309–317

    Article  CAS  PubMed  Google Scholar 

  • Molto E, Harrell RC (1993) Neural network classification of sweet potato embryos. Optics Agric Forest, Proc SPIE 1836:239–249

    Google Scholar 

  • Morimoto T, Hashimoto Y (2000) An intelligent control for greenhouse automation, oriented by the concepts of SPA and SFA – an application to a post-harvest process. Comput Electron Agric 29:3–20

    Article  Google Scholar 

  • Morimoto T, De Baerdemaeker J, Hashimoto Y (1997) An intelligent approach for optimal control of fruit-storage process using neural networks and genetic algorithms. Comput Electron Agric 18:205–224

    Article  Google Scholar 

  • Mozer MC, Smolensky P (1989) Using relevance to reduce network size automatically. Connect Sci 1:3–16

    Article  Google Scholar 

  • Nitze I, Schulthess U, Asche H (2012) Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th GEOBIA Janeiro, Brazil, 35–40

    Google Scholar 

  • Noguchi N, Terao H (1997) Path planning of an agricultural mobile robot by neural network and genetic algorithm. Comput Electron Agric 18:187–204

    Article  Google Scholar 

  • Osama K, Somvanshi P, Pandey AK, Mishra BN (2013) Modelling of nutrient mist reactor for hairy root growth using artificial neural network. Eur J Sci Res 97(4):516–526

    Google Scholar 

  • Prakash O, Mehrotra S, Krishna A, Mishra BN (2010) A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures. J Theor Biol 265:579–585

    Article  CAS  PubMed  Google Scholar 

  • Prasad V, Gupta SD (2006) Applications and potentials of artificial neural networks in plant tissue culture. In: Gupta SD, Ibaraki Y (eds) Plant tissue culture engineering. Springer Netherlands, pp 47–67

    Google Scholar 

  • Resop JP (2006) A comparison of artificial neural networks and statistical regression with biological resources applications. University of Maryland, College Park, USA

    Google Scholar 

  • Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD (1995) Artificial intelligence: a modern approach. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Ruan R, Xu J, Zhang C, Chi C-M, Hu W-S (1997) Classification of plant somatic embryos by using neural network classifiers. Biotechnol Prog 13:741–746

    Article  CAS  Google Scholar 

  • Salas J, Markus M, Tokar A (2000) Streamflow forecasting based on artificial neural networks. In: Govindaraju RS, Roa AR (eds) Artificial neural networks in hydrology. Springer Netherlands, pp 23–51

    Google Scholar 

  • Shao Q, Rowe RC, York P (2006) Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation. Eur J Pharm Sci 28(5):394–404

    Article  CAS  PubMed  Google Scholar 

  • Shi L, Duan Q, Ma X, Weng M (2012) The research of support vector machine in agricultural data classification. In: Li D, Chen Y (eds) Computer and computing technologies in agriculture V. Springer Berlin Heidelberg, pp 265–269

    Google Scholar 

  • Shigidi A, Garcia LA (2003) Parameter estimation in groundwater hydrology using artificial neural networks. J Comput Civ Eng 17:281–289

    Article  Google Scholar 

  • Stanimirovic PS, Miladinovic MB (2010) Accelerated gradient descent methods with line search. Numer Algorithms 54(4):503–520

    Article  Google Scholar 

  • Suroso MH, Tani A, Hoami N, Takigawa H, Nishiura Y (1996) Inverse technique for analysis of convective heat transfer over the surface of plant culture vessel. Trans ASAE 39:2277–2282

    Article  Google Scholar 

  • Tani A, Murase H, Kiyota M, Honami N (1992) Growth simulation of alfalfa cuttings in vitro by Kalman filter neural network. Acta Hort 319:671–676

    Google Scholar 

  • Tickle AB, Andrews R, Golea M, Diederich J (1998) The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Trans Neural Netw 9:1057–1068

    Article  CAS  PubMed  Google Scholar 

  • Uozumi N, Yoshino T, Shiotani S, Suehara K-I, Arai F, Fukuda T, Kobayashi T (1993) Application of image analysis with neural network for plant somatic embryo culture. J Ferment Bioeng 76:505–509

    Article  Google Scholar 

  • Venkataramanan L, Sigworth F (2002) Applying hidden Markov models to the analysis of single ion channel activity. Biophys J 82:1930

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • White H (1992) Artificial neural networks: approximation and learning theory. Blackwell Publishers, Inc., Oxford/Cambridge

    Google Scholar 

  • Widrow B, Hoff M (1960) Adaptive switching circuits. In: 1960 IRE WESCON convention record, vol 4. IRE, New York, pp 96–104

    Google Scholar 

  • Xiang C, Ding SQ, Lee TH (2005) Geometrical interpretation and architecture selection of MLP. IEEE Trans Neural Netw 16:84–96

    Article  PubMed  Google Scholar 

  • Yang ZR (2006) A novel radial basis function neural network for discriminant analysis. IEEE Trans Neural Netw 17:604–612

    Article  PubMed  Google Scholar 

  • Yao X (1999) Evolving artificial neural networks. Proc IEEE 87:1423–1447

    Article  Google Scholar 

  • Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214(1):32–48

    Article  Google Scholar 

  • Zhang C, Timmis R, Hu W-S (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 

Download references

Author information

Authors and Affiliations

  1. Department of Biotechnology, Institute of Engineering and Technology, G.B. Technical University, Sitapur Road, Lucknow, 226021, India

    Khwaja Osama Ph.D. & Bhartendu Nath Mishra Ph.D.

  2. Department of Biotechnology, Teri University, 10 Institutional Area, Vasant Kunj, New Delhi, 110070, India

    Pallavi Somvanshi Ph.D.

Authors
  1. Khwaja Osama Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhartendu Nath Mishra Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pallavi Somvanshi Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pallavi Somvanshi Ph.D. .

Editor information

Editors and Affiliations

  1. Department of Genomics, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, West Bengal, India

    Debmalya Barh

  2. University of Agriculture, Centre of Biochemistry and Biotechnology, Faisalabad, Pakistan

    Muhammad Sarwar Khan

  3. Department of Plant Biology, North Carolina State University, Raleigh, North Carolina, USA

    Eric Davies

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer India

About this chapter

Cite this chapter

Osama, K., Mishra, B.N., Somvanshi, P. (2015). Machine Learning Techniques in Plant Biology. In: Barh, D., Khan, M., Davies, E. (eds) PlantOmics: The Omics of Plant Science. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2172-2_26

Download citation

Publish with us

Policies and ethics

Search

Navigation