Open AccessIdentification of Fruits Using Deep Learning Approach
Author(s) -
Deepali M Bongulwar
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1049/1/012004
Subject(s) - artificial intelligence , computer science , convolutional neural network , sort , deep learning , identification (biology) , machine learning , feature extraction , feature (linguistics) , artificial neural network , quality (philosophy) , pattern recognition (psychology) , information retrieval , philosophy , linguistics , botany , biology , epistemology
The paper intends to build a Model for the identification and classification of fruits using the concept of deep learning. The objective is to build an automatic system for feature extraction using convolutional neural networks. The system can sort the fruits. It can be put-to-use in checking the condition of fruits, whether they are fresh or not. The self-service system in the retail market can use it to identify fruits. The proposed system uses high quality ‘ImageNet’ dataset. The dataset consists of five different categories of fruit images. Dataset is very challenging. The model uses Convolutional Neural Networks to identify fruits from images. The accuracy obtained is 92.23%. Deep learning outperforms machine learning algorithms.