Igor Kuivjogi Fernandes Data-verified
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Biography and Research Information
OverviewAI-generated summary
Igor Kuivjogi Fernandes' research focuses on the application of machine learning techniques to agricultural science, particularly in crop yield prediction and the characterization of breeding environments. His work integrates genetic and environmental data to model complex interactions, aiming to improve crop adaptation and breeding strategies. Fernandes has investigated these methods using case studies from Brazil, focusing on crops such as common bean and maize, as well as tropical irrigated rice.
His publications explore enviromic prediction as a tool to define climate adaptation limits and the use of machine learning for pattern recognition in environmental quality prediction. Fernandes also utilizes crop models in conjunction with machine learning to understand spatial-temporal characteristics of agricultural environments. His scholarship metrics include an h-index of 4, with 8 total publications and 83 citations. Key collaborators include Samuel B. Fernandes and Caio Canella Vieira, both from the University of Arkansas at Fayetteville, with whom he shares multiple publications.
Metrics
- h-index: 10
- Publications: 18
- Citations: 350
Selected Publications
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Genomic prediction and association mapping of early season flood tolerance in soybean (2025)
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Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay (2024)
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Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions (2024)
Collaboration Network
Top Collaborators
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil
- Environmental clusters defining breeding zones for tropical irrigated rice in Brazil
- Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil
- Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common beans in Brazil
Showing 5 of 6 shared publications
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil
- Environmental clusters defining breeding zones for tropical irrigated rice in Brazil
- Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil
- Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common beans in Brazil
Showing 5 of 6 shared publications
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil
- Environmental clusters defining breeding zones for tropical irrigated rice in Brazil
- Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil
- Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common beans in Brazil
Showing 5 of 6 shared publications
- Environmental clusters defining breeding zones for tropical irrigated rice in Brazil
- Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil
- Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil
- Data-Driven Machine Learning for Pattern Recognition Supports Environmental Quality Prediction for Irrigated Rice in Brazil
- Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
- Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
- Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
- Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
- Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
- Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common beans in Brazil
- Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil
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