Estimation of Fusarium Scab in Wheat Using Machine Vision and a Neural Network

Abstract

ABSTRACTA 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.