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Estimation of Fusarium Scab in Wheat Using Machine Vision and a Neural Network¹

¹Presented at the 1997 ASAE Annual Meeting in Minneapolis, MN. Paper 973042. ²Department of Biosystems and Agricultural Engineering, University of Minnesota, 1390 Eckles Ave., St. Paul, MN 55108. ³Corresponding author. E-mail: rruan@rabbit.bae.umn.edu ⁴Department of Mechanical and Electrical Engineering, Shandong Institute of Light Industry, P.R. China. Currently visiting research associate at Department of Biosystems and Agricultural Engineering, University of Minnesota. ⁵Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455. ⁶Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108.

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.