Seyaed Ehsan Yasrebi; Iman Zabbah; Behnaz Behzadiyan; Ali Maroosi; Roya Rezaie
Abstract
Saffron is an important commercial good in Iran and it is important to pay attention to its mechanization from production to packaging. Upon arrival of the saffron to the laboratory's qualitative process, an initial assessment is carried out by an expert on the basis of the apparent features. However, ...
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Saffron is an important commercial good in Iran and it is important to pay attention to its mechanization from production to packaging. Upon arrival of the saffron to the laboratory's qualitative process, an initial assessment is carried out by an expert on the basis of the apparent features. However, human error in determining the quality of saffron based on its apparent features is inevitable; use of artificial intelligence techniques can be effective in reducing human errors while mechanizing the system. It was a diagnostic study and its database consisted of 113 samples of saffron with 7 features, which were collected by the researchers on October 2016 from the valid laboratory of Saffron and under the supervision of an expert. Sample qualitative analysis was performed with the help of features in 4 different classes including excellent, good, average and second grade average. Artificial neural networks have been used to classify saffron. After analyzing and comparing the generated models using multilayer perceptron neural networks and learning vector neural network, the highest accuracy of classification on the training and testing samples was obtained with 75.93 and 75.75%, respectively. The accuracy obtained indicated that the multi-layer perceptron neural network model can be used as a decision maker by an expert or independently in saffron lab centers.
