There are various applications using computer-aided quality controlling system. In this study, seed data set acquired from UCI machine learning database was used. The purpose of the study is to perform the operations for separation of seed species from each other in the seed data set. Three different seed whose data was acquired from the UCI machine learning database was used. Later it was classified by applying the methods of KNN, Naive Bayes, J48 and multilayer perceptron to the dataset.

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... wheat seed data received from the UCI machine learning database was classified, WEKA program was used. By changing the number of neurons, the highest classification success rate was achieved when the number of neuron was 7. The success rate with 7 neurons was 97.17%. When the classification success rate was calculated according to KNN for the different number of neighbors, the highest success rate was obtained as 95.71% for 4 neighbors.