Classification of Saffron Based on Its Apparent Characteristics Using Artificial Neural Networks

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Yasrebi Seyed Ehsan; Zabah Iman; Behzadian Behnaz; Marousi Ali; REZAEI ROYA

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Journal: Saffron Agronomy and Technology Year:2020 | Volume:7 | Issue:4 Page(s): 521-535

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Title

Classification of Saffron Based on Its Apparent Characteristics Using Artificial Neural Networks

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Keywords

Saffron classificationQ1

Artificial Neural NetworkQ1

Artificial intelligenceQ1

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 saffron to the laboratory's qualitative process, an initial assessment is carried out by an expert on the basis of its 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. This research 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 credible Saffron laboratory under the supervision of an expert. Sample qualitative analysis was performed with the help of features in four different classes including excellent, good, average and second grade average. Artificial Neural Networks was 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 to be 75. 93 and 75. 75%, respectively. The accuracy obtained indicated that the multi-layer perceptron neural network model can be used as a decision-making tool by an expert or independently in saffron lab centers.

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APA: Copy

Yasrebi, Seyed Ehsan, Zabah, Iman, Behzadian, Behnaz, Marousi, Ali, & REZAEI, ROYA. (2020). Classification of Saffron Based on Its Apparent Characteristics Using Artificial Neural Networks. SAFFRON AGRONOMY AND TECHNOLOGY, 7(4 ), 521-535. SID. https://sid.ir/paper/373065/en

Vancouver: Copy

Yasrebi Seyed Ehsan, Zabah Iman, Behzadian Behnaz, Marousi Ali, REZAEI ROYA. Classification of Saffron Based on Its Apparent Characteristics Using Artificial Neural Networks. SAFFRON AGRONOMY AND TECHNOLOGY[Internet]. 2020;7(4 ):521-535. Available from: https://sid.ir/paper/373065/en

IEEE: Copy

Seyed Ehsan Yasrebi, Iman Zabah, Behnaz Behzadian, Ali Marousi, and ROYA REZAEI, “Classification of Saffron Based on Its Apparent Characteristics Using Artificial Neural Networks,” SAFFRON AGRONOMY AND TECHNOLOGY, vol. 7, no. 4 , pp. 521–535, 2020, [Online]. Available: https://sid.ir/paper/373065/en

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