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Big Data Revolution and Machine Learning to Solve Genetic Mysteries in Crop Breeding

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Sustainable Agriculture in the Era of the OMICs Revolution

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Abstract

Modern-day OMICs and technological advancement have enabled complex analysis of various phenotypic and genotypic characters more feasible resulting in a massive explosion of data relevant to various plant characters. This large collection of useful data has also propelled the scientific community toward expressive research that opens up multiple avenues of plant sciences in order to deal with modern challenges and threats faced by global food security. Issues such as climate change, increasing population, resource scarcity, and plant diseases exhibit an existential threat to the human race. Therefore, as a result, improving the yield potential of major food crops in a sustainable manner is necessary to meet these challenges. In parallel to this, the field of machine learning has evolved rapidly in recent times and has been massively integrated into different fields of science including plant sciences. This recent advancement in machine learning models has enabled a better understanding of genotypic basis and genome-wide analysis for various phenotypic traits in plants. Consequently, screening out useful traits can be utilized further to unlock the potential for breeding modern crops.

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Authors and Affiliations

  1. Department of Plant Pathology, Faculty of Agriculture, University of Agriculture Faisalabad (UAF), Faisalabad, Pakistan

    Faizan Ali

  2. Department of Plant Pathology, Faculty of Agriculture, University of Agriculture Faisalabad sub-campus Depalpur Okara (UAF-DO), Faisalabad, Pakistan

    Sohaib Sarfraz

  3. Department of Plant Pathology, Institute of Plant Protection MNS-University of Agriculture Multan, Multan, Pakistan

    Akhtar Hameed

  4. Department of Plant Breeding and Genetics/Biotechnology, U.S.-Pakistan Center for Advanced Studies in Agriculture and Food Security (USPCAS-AFS) University of Agriculture Faisalabad, Faisalabad, Pakistan

    Zaheer Ahmad

Authors
  1. Faizan Ali
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  2. Sohaib Sarfraz
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  3. Akhtar Hameed
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  4. Zaheer Ahmad
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Corresponding author

Correspondence to Sohaib Sarfraz .

Editor information

Editors and Affiliations

  1. College of Arts & Sciences, Tuskegee University, Tuskegee, AL, USA

    Channa S. Prakash

  2. Deptartment of Plant Breeding and Genetics, The University of Haripur, Haripur, Pakistan

    Sajid Fiaz

  3. Faculty of Agricultural Science and Technology, Sivas University of Science and Technology, Sivas, Turkey

    Muhammad Azhar Nadeem

  4. Faculty of Agricultural Science and Technology, Sivas University of Science and Technology, Sivas, Turkey

    Faheem Shehzad Baloch

  5. Department of Agronomy, The University of Haripur, Haripur, Pakistan

    Abdul Qayyum

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Ali, F., Sarfraz, S., Hameed, A., Ahmad, Z. (2023). Big Data Revolution and Machine Learning to Solve Genetic Mysteries in Crop Breeding. In: Prakash, C.S., Fiaz, S., Nadeem, M.A., Baloch, F.S., Qayyum, A. (eds) Sustainable Agriculture in the Era of the OMICs Revolution. Springer, Cham. https://doi.org/10.1007/978-3-031-15568-0_4

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