Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

Abstract Background Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using...

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Bibliographic Details
Main Authors: Michael Gomez Selvaraj, Manuel Valderrama, Diego Guzman, Milton Valencia, Henry Ruiz, Animesh Acharjee
Format: Article
Language:English
Published: BMC 2020-06-01
Series:Plant Methods
Subjects:
Automated aerial image processing
Above-ground biomass
Cassava
Machine learning
Multispectral UAV imagery
Root yield prediction
Online Access:http://link.springer.com/article/10.1186/s13007-020-00625-1

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http://link.springer.com/article/10.1186/s13007-020-00625-1

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