Abstract
Key message
The optimization of plant hormone application to achieve better rhizogenesis in eucalypt cuttings has been demonstrated using a machine learning approach.
Abstract
This study focuses on reducing the human bias in treatment selection in case of adventitious rhizogenesis in eucalypts. The effect of different concentrations of indole-3-butyric acid (IBA) on rhizogenesis was studied and differential responses of genotypes to treatments were observed. Stem cuttings of six different Eucalyptus genotypes were administered with different concentrations and duration of IBA treatments and subsequently multiple parameters, like total length of root system (TLRS), number of roots, shoot to root ratio, etc. were measured using image analysis and manual measurements. A rule-based model and classification tree forming the basis for the C5.0 algorithm was used to eliminate “Human Bias” from the selection procedure. A top-down greedy search approach applied in the whole training set which selected the best feature as the root node and resulted in splitting the data set into smaller subsets. It was subsequently compared with our multiple-attribute decision-making (MADM) model i.e. GRA. The data were trained and tested, and the confidence value report demonstrated a high accuracy level of the decision trees. This suggests the potential use of artificial intelligence in clonal forestry. As per our results, lowering the concentration of auxin and increasing the duration of exposure produced better root quality in E. camaldulensis and hybrids (reciprocal hybrids of E. tereticornis and E. camaldulensis), while the opposite effect was observed in E. tereticornis clones. Therefore, the use of machine learning algorithms could significantly increase the accuracy of treatment selection and provide the optimum results for individual genotypes.
Similar content being viewed by others
References
Atkinson CJ, Michaelangelo P, Webster AD, Kuden AM (1999) Drought tolerance of apple rootstocks: Production and partitioning of dry matter. Plant Soil 206:223–235. https://doi.org/10.1023/A:1004415817237
Che D, Liu Q, Rasheed K, Tao X (2011) Software Tools and Algorithms for Biological Systems 696:191–199. https://doi.org/10.1007/978-1-4419-7046-6
Da Costa CT, De Almeida MR, Ruedell CM, Schwambach J, Maraschin FS, Fett-Neto AG (2013) When stress and develop- ment go hand in hand: main hormonal controls of adventitious rooting in cuttings. Front Plant Sci 4:133. https://doi.org/10.3389/fpls.2013.00133
De Almeida MR, Aumond M, Da Costa CT, Schwambach J, Ruedell CM, Correa LR, Fett-Neto AG (2017) Environmental control of adventitious rooting in Eucalyptus and Populus cuttings. Trees Struc Func 31(5):1377–1390. https://doi.org/10.1007/s00468-017-1550-6
Deng J (1982) Control problems of grey systems. Syst Cont Lett 1:288–294
Devijver PA, Kittler J (1982) Pattern Recognition: A Statistical Approach, Prentice Hall
Douglas GB, McIvor IR, Lloyd-West CM (2016) Early root development of field-grown poplar: effects of planting material and genotype. New Zeal J For Sci 46:1. https://doi.org/10.1186/s40490-015-0057-4
Druege U, Hilo A, Pérez-Pérez JM, Klopotek Y, Acosta M, Shahinnia F, Hajirezaei MR (2019) Molecular and physiological control of adventitious rooting in cuttings: phytohormone action meets resource allocation. Ann Bot. https://doi.org/10.1093/aob/mcy234
FAO (2014) The state of the world’s forest genetic resources. FAO, Rome
De Assis F, Fett-Neto AG, Alfenas AC (2004) Current techniques and prospects for the clonal propagation of hardwoods with emphasis on Eucalyptus. In Research Signpost ISBN: 81-7736-228-3
Henderson R, Ford ED, Renshaw E (1983) Morphology of the structural root system of sitka spruce 2 Computer Simulation of Rooting Patterns. Forestry Int J Forest Res 56(2):137–153. https://doi.org/10.1093/forestry/56.2.137
Kelchtermans P (2014) Machine learning applications in proteomics research: how the past can boost the future. Proteomics 14:353–366
Kim SH, Kang MS, Jung YG (2016) Big data analysis using python in agriculture forestry and fisheries. Int J Adv Smart Converg 5(1):47–50. https://doi.org/10.7236/IJASC.2016.5.1.47
Kuhn M (2013) Classification using C5.0 Use R! Groton CT: Pfizer Global R&D
Kulkarni HD, Lal P (1995) Performance of Eucalyptus clones at ITC Bhadrachalam India. In: CRC-IUFRO Conference on Eucalyptus Plantations Improving Fibre Yield and Quality, Hobart, 19–24. Proceedings edited by Reid JB, Cromer RN, Tibbits WN and Raymond CA. Hobart, CRCTHF. pp. 274–275
Kuo Y, Yang T, Huang GW (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93. https://doi.org/10.1016/j.cie.2007.12.002
Lal P, Dogra AS, Sharma SC and Chahal GBS (2006) Evaluation of different clones of eucalyptus in Punjab. Indian Forester 132(11)
Ma C, Zhang HH, Wang X (2014) Machine learning for Big data analytics in plants. Trends Plant Sci 19(12):798–808. https://doi.org/10.1016/j.tplants.2014.08.004
Madhu M, Hatfeld JL (2013) Dynamics of plant root growth under increased atmospheric carbon dioxide. Agron J 105(3):657–669. https://doi.org/10.2134/agronj2013.0018
Misra RK, Dexter AR, Alston AM (1986) Maximum axial and radial growth pressures of plant roots. Plant Soil 95:315–326
Nair PKR, Rao MR, Buck LE (Eds.) (2004) New Vistas in Agroforestry. Adv Agroforestry
Orhan E, Esitken A, Ercisli S (2007) Sahin F (2015) Radicle tip-cutting on lateral root induction in Pistacia vera. J Hortic Sci Biotechnol 10(1080/14620316):11512190
Pang S, Gong J (2010) C5.0 Classification algorithm and application on individual credit evaluation of banks. Syst Eng Theory Practice 29(12):94–104. https://doi.org/10.1016/s1874-8651(10)60092-0
Park YS, Bonga JM, Moon HK (2016) Vegetative propagation of forest trees. National Institute of Forest Science. https://www.iufro.org/science/divisions/division-2/20000/20900/20902/publications
Quinlan JR (1993) C4.5: programs for machine learning. vol. 1. California: Morgan Kaufmann
Ragonezzi C, Klimaszewska K, Castro MR, Lima M, De Oliveira P, Zavattieri MA (2010) Adventitious rooting of conifers: influence of physical and chemical factors. Trees Struct Funct 24:975–992. https://doi.org/10.1007/s00468-010-0488-8
Sabatino L, D’Anna F, Iapichino G (2014) Cutting type and IBA treatment duration affect Teucrium fruticans adventitious root quality. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 42(2):478–481. https://doi.org/10.1583/nbha4229611
Saeys Y (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517
Saha R, Ginwal HS, Chandra G, Barthwal S (2019) Integrated assessment of adventitious rhizogenesis in Eucalyptus; root quality index and rooting dynamics. J Forestry Res. https://doi.org/10.1007/s11676-019-01040-6
Siknun GP, Sitanggang IS (2016) Web-based classification application for forest fire data using the shiny framework and the C5.0 algorithm. Procedia Environmen Sci 33:332–339. https://doi.org/10.1016/j.proenv.2016.03.084
Tarca AL, Carey VJ, Chen X, Romero R, Drǎghici S (2007) Machine learning and its applications to biology. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.0030116
Yasar E, Sezai E, Ayhan H, Ramazan C (2010) Effects of plant growth promoting rhizobacteria (PGPR) on rooting and root growth of kiwifruit (Actinidia deliciosa) stem cuttings. Biol Res. https://doi.org/10.4067/S0716-97602010000100011
Acknowledgement
The authors would like to thank Forest Research Institute, Dehradun and DST-INSPIRE programme for funding this work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest in this research work.
Ethical statement
This research does not include any human subjects and the ethical standards of the journal have been followed in this manuscript.
Additional information
Communicated by Grote.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Saha, R., Ginwal, H.S., Chandra, G. et al. A comparative study on grey relational analysis and C5.0 classification algorithm on adventitious rhizogenesis of Eucalyptus. Trees 35, 43–52 (2021). https://doi.org/10.1007/s00468-020-02008-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00468-020-02008-4