Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective...

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Bibliographic Details
Main Authors: Juan Camilo Henao-Rojas, María Gladis Rosero-Alpala, Carolina Ortiz-Muñoz, Carlos Enrique Velásquez-Arroyo, William Alfonso Leon-Rueda, Joaquín Guillermo Ramírez-Gil
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Plants
Subjects:
random forest
K-means
morphological descriptors
digital agriculture
data science
Online Access:https://www.mdpi.com/2223-7747/10/2/247

Internet

https://www.mdpi.com/2223-7747/10/2/247

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