AUTOMATIC CLASSIFICATION OF AGRICULTURAL GRAINS: COMPARISON OF NEURAL NETWORKS


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Kayabasi A., Toktas A., Sabanci K., Yigit E.

NEURAL NETWORK WORLD, vol.28, no.3, pp.213-224, 2018 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 3
  • Publication Date: 2018
  • Doi Number: 10.14311/nnw.2018.28.013
  • Title of Journal : NEURAL NETWORK WORLD
  • Page Numbers: pp.213-224
  • Keywords: classification, agricultural grains, wheat grains, neural networks, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), COMPUTER VISION, FUZZY, IDENTIFICATION, VARIETIES, FEATURES, SYSTEM, WHEAT, ALGORITHM, COFFEE

Abstract

In this study, applications of well-known neural networks such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) for wheat grain classification into three species are comparatively presented. The species of wheat grains which are Kama (#70), Rosa (#70) and Canadian (#70) are designated as outputs of neural network models. The classification is carried out through data of wheat grains (#210) acquired using X-ray technique. The data set includes seven grain's geometric parameters: Area, perimeter, compactness, length, width, asymmetry coefficient and groove length. The neural networks input with the geometric parameters are trained through 189 wheat grain data and their accuracies are tested via 21 data. The performance of neural network models is compared to each other with regard to their accuracy, efficiency and convenience. For testing data, the ANN, ANFIS and SVM models numerically calculate the outputs with mean absolute error (MAE) of 0.014, 0.018 and 0.135, and classify the grains with accuracy of 100%, 100% and 95.23%, respectively. Furthermore, data of 210 grains is synthetically increased to 3210 in order to investigate the proposed models under big data. It is seen that the models are more successful if the size of data is increased, as well. These results point out that the neural networks can be successfully applied to classification of agricultural grains whether they are properly modelled and trained.