Abstract:
In this research work, a digital image analysis technique was used to classify malt barley and
food barley based on their color, morphological and textural features. From each sample, 50
images were captured. Three hundred images were captured from samples collected from Srinka
Agricultural Research Center (SARC). Artificial neural network was used to classify barley
seeds and to cheek weather the samples were correctly classifies to their origin of collection or
not. For the analysis, 12 color, 6morphological and 12 textural features, totally 30 features were
extracted from images of barley samples. Three experimental classification setups were applied
for classification designs by using morphology, color and texture. For all setups 300images
were used as inputs of the machine. From these datasets, 240 images were used for training the
machine while the remaining 60(20%) images were used for testing and validation. The
accuracy of classification using morphological, texture and color feature from for all confusion
matrixes was 83.3%. The seeds percentage of samples, which were not correctly, classified were
16.7% for color, texture and morphological features
