Estimation of Fusarium scab in wheat using machine vision and a neural network

R. R Ruan, Shu Ning, Aijun Song, Anrong Ning, Roger Jones, Paul L Chen

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)455-459
Number of pages5
JournalCereal Chemistry
Volume75
Issue number4
DOIs
StatePublished - Jan 1 1998

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