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Estimation of Fusarium Scab in Wheat Using Machine Vision and a Neural Network
Author(s) -
Ruan Roger,
Ning Shu,
Song Aijun,
Ning Anrong,
Jones Roger,
Chen Paul
Publication year - 1998
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.1998.75.4.455
Subject(s) - artificial neural network , artificial intelligence , backpropagation , correlation coefficient , machine vision , pattern recognition (psychology) , texture (cosmology) , computer vision , computer science , machine learning , image (mathematics)
A neural network was used to relate color and texture features of wheat samples to damage caused by Fusarium scab infection. A total of 55 color and texture features were extracted from images captured by a machine vision system. Random errors were reduced by using average values of features from multiple images of individual samples. A four‐layer backpropagation neural network was used. The percentage of visual scabby kernels (%VSK) estimated by the trained network followed the actual percentage with a correlation coefficient of 0.97; maximum and mean absolute errors were 5.14 and 1.93%, respectively. A comparison between the results by the machine vision‐neural network technique and the human expert panel led to the conclusion that the machine vision‐neural network technique produced more accurate determination of %VSK than the human expert panel.