Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield pe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/14/4/777 |
| _version_ | 1797605383631536128 |
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| author | Mohsen Yoosefzadeh Najafabadi Mohsen Hesami Milad Eskandari |
| author_facet | Mohsen Yoosefzadeh Najafabadi Mohsen Hesami Milad Eskandari |
| author_sort | Mohsen Yoosefzadeh Najafabadi |
| collection | DOAJ |
| description | In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change. |
| first_indexed | 2024-03-11T05:00:19Z |
| format | Article |
| id | doaj.art-a60a29f36621475ab578a67f626e7515 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-11T05:00:19Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-a60a29f36621475ab578a67f626e75152023-11-17T19:22:16ZengMDPI AGGenes2073-44252023-03-0114477710.3390/genes14040777Machine Learning-Assisted Approaches in Modernized Plant Breeding ProgramsMohsen Yoosefzadeh Najafabadi0Mohsen Hesami1Milad Eskandari2Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, CanadaDepartment of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, CanadaDepartment of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, CanadaIn the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.https://www.mdpi.com/2073-4425/14/4/777artificial intelligencebigdatacomplex traitsdata-integration strategiesdeep learningensemble learning |
| spellingShingle | Mohsen Yoosefzadeh Najafabadi Mohsen Hesami Milad Eskandari Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs Genes artificial intelligence bigdata complex traits data-integration strategies deep learning ensemble learning |
| title | Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs |
| title_full | Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs |
| title_fullStr | Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs |
| title_full_unstemmed | Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs |
| title_short | Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs |
| title_sort | machine learning assisted approaches in modernized plant breeding programs |
| topic | artificial intelligence bigdata complex traits data-integration strategies deep learning ensemble learning |
| url | https://www.mdpi.com/2073-4425/14/4/777 |
| work_keys_str_mv | AT mohsenyoosefzadehnajafabadi machinelearningassistedapproachesinmodernizedplantbreedingprograms AT mohsenhesami machinelearningassistedapproachesinmodernizedplantbreedingprograms AT miladeskandari machinelearningassistedapproachesinmodernizedplantbreedingprograms |