NDLI: Separating Pistachio Varieties Using Automatic Trainable Classifier

Content Provider	IEEE Xplore Digital Library
Author	Omid, M. Mahmoudi, A. Aghagolzadeh, A. Borghaee, A.M.
Copyright Year	2007
Description	Author affiliation: Univ. of Tehran, Karaj (Omid, M.)
Abstract	In this study an automatic trainable classifier, based on artificial neural network (ANN), for separating four different varieties of pistachio nuts is presented. In total 3200 sample data were selected in the development of the ANN models. Signal analysis procedures from time-domain and frequency-domain as well as statistical procedures were used for feature extraction. By performing principal component analysis on the input data set, more than 98% reduction in the dimension of the feature vector was achieved. Altogether 40 features were selected as input vector to ANN models. In order to find optimal configuration, several architectures, each having different number of neurons in the hidden layer, were designed and evaluated. The ANN models were trained using the gradient descent with momentum learning rule. Optimal configuration had a 40-12-4 structure, i.e., a network having one hidden layer with 12 neurons. This selection was based on the minimization of mean square error (MSE) and correct separation rates (CSR). The estimated MSE and CSR were 0.018 and 97.5%, respectively. I.e., only 2.5 % of pistachio nuts were misclassified.
Starting Page	44
Ending Page	44
File Size	202702
Page Count	1
File Format	PDF
ISBN	0769528597
DOI	10.1109/CONIELECOMP.2007.97
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-19
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	frequency-domain System testing pistachio Neurons Artificial neural networks neural networks sound Time domain analysis Agricultural engineering classification Sorting feature extraction Feature extraction Agriculture Signal analysis Principal component analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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