Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis

Open Access

Peer-reviewed

Research Article

About the Authors

Zhanyou Xu

Roles Conceptualization, Data curation, Formal analysis, Investigation, Software, Writing – original draft

Affiliation Plant Science Research Unit, USDA, Agricultural Research Service, Saint Paul, MN, United States of America

Andreomar Kurek

Roles Data curation, Software, Validation

Affiliation Department of Agronomy, Iowa State University, Ames, IA, United States of America

Steven B. Cannon

Roles Conceptualization, Methodology, Validation, Writing – review & editing

Affiliation Corn Insects, and Crop Genetics Research Unit, USDA, Agricultural Research Service, Ames, IA, United States of America

William D. Beavis

Roles Conceptualization, Project administration, Resources, Supervision, Writing – review & editing

Affiliation Department of Agronomy, Iowa State University, Ames, IA, United States of America

The authors have declared that no competing interests exist.

Artificial neural networks
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Machine learning algorithms
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Genetics
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Soybeans
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Genomics
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Support vector machines
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Machine learning
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Plant breeding
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