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Applications of machine learning in plant biotechnology
Israel Ogwuche Ogra
, Yardjouma Silue
,
Olaniyi Amos Fawole
, Adeyemi Oladapo Aremu
, Umezuruike Linus Opara
Botany and Plant Biotechnology
UNESCO International Centre for Biotechnology
Tomsk State University
University of Johannesburg
North West University
University of KwaZulu-Natal
Stellenbosch University
Research output
:
Contribution to journal
›
Review article
›
peer-review
2
Citations (Scopus)
Overview
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Keyphrases
Machine Learning Applications
100%
Plant Biotechnology
100%
Machine Learning
57%
Full Potential
28%
Model Selection
28%
Machine Learning Technology
28%
Genetic Engineering
14%
Bioprocess
14%
Proteomics
14%
Agriculture
14%
Tissue Culture
14%
Successful Integration
14%
Technology Integration
14%
Performance Evaluation
14%
Enhanced Data
14%
Deployment Strategy
14%
Ensemble Model
14%
Selection Performance
14%
Deep Learning Algorithm
14%
Data Availability
14%
Systemic Integration
14%
Data Standardization
14%
Practical Deployment
14%
Plant Tissue Culture
14%
Model Transferability
14%
Plant Phenomics
14%
Plant Phenotyping
14%
Across Domains
14%
Deployment Constraints
14%
Biopharmaceutical Manufacturing
14%
Image-based Diagnostics
14%
Agricultural and Biological Sciences
Learning System
100%
Machine Learning
100%
Biotechnology
100%
Proteomics
12%
Tissue Culture
12%
Bioprocessing
12%
Deep Learning Method
12%
Plant Tissue Culture
12%
Phenomics
12%
Immunology and Microbiology
Plant Biotechnology
100%
Tissue Culture
14%
Plant Tissue Culture
14%
Proteomics
14%
Genetic Engineering
14%
Biochemistry, Genetics and Molecular Biology
Plant Biotechnology
100%
Tissue Culture
14%
Proteomics
14%
Genetic Engineering
14%
Plant Tissue Culture
14%
Earth and Planetary Sciences
Machine Learning
100%
Bioprocessing
12%
Genetic Engineering
12%
Material Science
Proteomics
100%