Open AccessFruit Maturity Classification Using Convolutional Neural Networks Method
Author(s) -
Selly Anatya,
Viny Christanti Mawardi,
Janson Hendryli
Publication year - 2020
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/1007/1/012149
Subject(s) - convolutional neural network , artificial intelligence , pattern recognition (psychology) , maturity (psychological) , computer science , image retrieval , content based image retrieval , contextual image classification , image (mathematics) , artificial neural network , psychology , developmental psychology
Difficulty in finding information about levels maturity based on the type of fruit using data textual, make a search system using image as query needed. The concept of Content-Based Image Retrieval (CBIR) will search and display images again which are relevant based on the visual features query image. In this study an application was made to classify 5 classes of fruit, Star fruit, Mango, Melon, Banana and Tomato. Which in each class divided again into 52 subclasses consisting of the type and level of fruit maturity with a total of 5030 training data images. The method used to classify and extract images features is Convolutional Neural Network (CNN). After the image is classified, the search process is carried out to determine the fruit that is similar to the classified image. The results of the classification accuracy of 1294 images are 61%. While the retrieval of 50 images has a precision value of 88.93%.