Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases

We discuss the use of matrix relevance learning, a popular extension to prototype learning algorithms, applied to a three-class classification task of diagnosing cassava diseases from spectral data. Previously this diagnosis has been done using plant image data taken with a smartphone. However for t...

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Bibliographic Details
Main Authors: Godliver Owomugisha, Friedrich Melchert, Ernest Mwebaze, John. A. Quinn, Michael Biehl
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Cassava disease diagnosis
feature selection
matrix relevance learning
spectral data
Online Access:https://ieeexplore.ieee.org/document/9448029/

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