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Machine Learning for Plant Breeding and Biotechnology

Author

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  • Mohsen Niazian

    (Field and Horticultural Crops Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Jam-e Jam cross way, P.O. Box 741, 66169-36311 Sanandaj, Iran)

  • Gniewko Niedbała

    (Department of Biosystems Engineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

Abstract

Classical univariate and multivariate statistics are the most common methods used for data analysis in plant breeding and biotechnology studies. Evaluation of genetic diversity, classification of plant genotypes, analysis of yield components, yield stability analysis, assessment of biotic and abiotic stresses, prediction of parental combinations in hybrid breeding programs, and analysis of in vitro-based biotechnological experiments are mainly performed by classical statistical methods. Despite successful applications, these classical statistical methods have low efficiency in analyzing data obtained from plant studies, as the genotype, environment, and their interaction (G × E) result in nondeterministic and nonlinear nature of plant characteristics. Large-scale data flow, including phenomics, metabolomics, genomics, and big data, must be analyzed for efficient interpretation of results affected by G × E. Nonlinear nonparametric machine learning techniques are more efficient than classical statistical models in handling large amounts of complex and nondeterministic information with “multiple-independent variables versus multiple-dependent variables” nature. Neural networks, partial least square regression, random forest, and support vector machines are some of the most fascinating machine learning models that have been widely applied to analyze nonlinear and complex data in both classical plant breeding and in vitro-based biotechnological studies. High interpretive power of machine learning algorithms has made them popular in the analysis of plant complex multifactorial characteristics. The classification of different plant genotypes with morphological and molecular markers, modeling and predicting important quantitative characteristics of plants, the interpretation of complex and nonlinear relationships of plant characteristics, and predicting and optimizing of in vitro breeding methods are the examples of applications of machine learning in conventional plant breeding and in vitro-based biotechnological studies. Precision agriculture is possible through accurate measurement of plant characteristics using imaging techniques and then efficient analysis of reliable extracted data using machine learning algorithms. Perfect interpretation of high-throughput phenotyping data is applicable through coupled machine learning-image processing. Some applied and potentially applicable capabilities of machine learning techniques in conventional and in vitro-based plant breeding studies have been discussed in this overview. Discussions are of great value for future studies and could inspire researchers to apply machine learning in new layers of plant breeding.

Suggested Citation

  • Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:436-:d:420461
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    References listed on IDEAS

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    1. Haifei Hu & Armin Scheben & David Edwards, 2018. "Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline," Agriculture, MDPI, vol. 8(6), pages 1-18, May.
    2. SangSik Lee & YiNa Jeong & SuRak Son & ByungKwan Lee, 2019. "A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning," Sustainability, MDPI, vol. 11(13), pages 1-21, July.
    3. Gniewko Niedbała & Danuta Kurasiak-Popowska & Kinga Stuper-Szablewska & Jerzy Nawracała, 2020. "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain," Agriculture, MDPI, vol. 10(4), pages 1-12, April.
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    Cited by:

    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    2. Meenakshi Sharma & Prashant Kaushik & Aakash Chawade, 2021. "Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research," Sustainability, MDPI, vol. 13(15), pages 1-14, August.
    3. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    4. Siraj Osman Omer, 2021. "Application of Bayesian Networks of Genotype by Environment Interaction Evaluation Under Plant Disease, Soil Types and Climate Condition-using Bayesia Lab," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 7(3), pages 158-166, 07-2021.
    5. Edina Csákvári & Melinda Halassy & Attila Enyedi & Ferenc Gyulai & József Berke, 2021. "Is Einkorn Wheat ( Triticum monococcum L.) a Better Choice than Winter Wheat ( Triticum aestivum L.)? Wheat Quality Estimation for Sustainable Agriculture Using Vision-Based Digital Image Analysis," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    6. Mohammad Rokhafrouz & Hooman Latifi & Ali A. Abkar & Tomasz Wojciechowski & Mirosław Czechlowski & Ali Sadeghi Naieni & Yasser Maghsoudi & Gniewko Niedbała, 2021. "Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat," Agriculture, MDPI, vol. 11(11), pages 1-24, November.
    7. Gniewko Niedbała & Danuta Kurasiak-Popowska & Magdalena Piekutowska & Tomasz Wojciechowski & Michał Kwiatek & Jerzy Nawracała, 2022. "Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean ( Glycine max [L.] Merrill) Cultivar Augusta," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    8. Mohsen Sabzi-Nojadeh & Gniewko Niedbała & Mehdi Younessi-Hamzekhanlu & Saeid Aharizad & Mohammad Esmaeilpour & Moslem Abdipour & Sebastian Kujawa & Mohsen Niazian, 2021. "Modeling the Essential Oil and Trans -Anethole Yield of Fennel ( Foeniculum vulgare Mill. var. vulgare ) by Application Artificial Neural Network and Multiple Linear Regression Methods," Agriculture, MDPI, vol. 11(12), pages 1-17, November.
    9. Juan Diego Valenzuela-Cobos & Fabricio Guevara-Viejó & Purificación Vicente-Galindo & Purificación Galindo-Villardón, 2023. "Eco-Friendly Biocontrol of Moniliasis in Ecuadorian Cocoa Using Biplot Techniques," Sustainability, MDPI, vol. 15(5), pages 1-12, February.

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