Automatic fruit classification using support vector machines: a comparison with artificial neural network

Content Provider	Scilit
Author	Astuti, Winda Dewanto, Satrio Soebandrija, Khristian Edi Nugroho Tan, Sofyan
Copyright Year	2018
Description	Journal: Iop Conference Series: Earth and Environmental Science Food processing Technologies play important roles in supporting Making Indonesia (MI) 4.0. The effectiveness in food processing becomes emerging in the food industry. The automatic fruit classification system is becoming important for use in many food processing industries. A novel approach for classifying, using support vector machines (SVM) is presented in this paper. Fruit classification based on their shape is proposed in this work. The system differentiates the fruit based on their shape. Fast Fourier Transform (FFT) is extracted and later used as input to the SVM-based identifier. The fruit parameters are compared and classified, the results of computer simulation show that this technique produces better accuracy than that of the existing technique that is based on artificial neural network (Ann). The SVM also requires less training time than artificial neural network (Ann).
Related Links	http://iopscience.iop.org/article/10.1088/1755-1315/195/1/012047/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/195/1/012047
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	1
Volume Number	195
Language	English
Publisher	IOP Publishing
Publisher Date	2018-12-14
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Artificial Neural Network
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science