One of the many indispensable tools for ensuring a
quality product is variety identification. Human experts identify
the product variety by personal observation; however, in their
absence and rarity, technology can be used instead. This paper
proposed to develop a technique that can be used to determine
the type of cassava through its digital leaf image. There are 235
images used for testing and preprocessing. Two images for each
of the 47 cassava varieties are used in this study. The
preprocessing method was performed first before the extraction
of features. The Otsu algorithm segments the leaf image from
the background. From the leaf samples, nine (9) color features,
three (3) morphological features and, three (3) shape features
were extracted. The values of the 15 extracted features are the
input for the system for variety recognition. Backpropagation
method of the artificial neural network (ANN) of multilayer
perceptron is used to train the system. For the input, hidden, and
output layers, the values are 15, 30, and 47, respectively. These
correspond to the 15 extracted features, 30 hidden layers, and 47
cassava varieties. The accuracy obtained in the experiment is
85.11%. It can be concluded that the technology was able to
identify the different cassava varieties effectively