Open AccessSistem Identifikasi Tingkat Kematangan Buah Nanas Secara Non-Destruktif Berbasis Computer Vision
Author(s) -
Nevalen Aginda Prasetyo,
Arif Surtono,
Junaidi Junaidi,
Gurum Ahmad Pauzi
Publication year - 2021
Publication title -
journal of energy, material, and instrumentation technology
Language(s) - English
Resource type - Journals
eISSN - 2747-299X
pISSN - 2747-2043
DOI - 10.23960/jemit.v2i1.26
Subject(s) - kurtosis , artificial intelligence , artificial neural network , computer science , rgb color model , pattern recognition (psychology) , skewness , maturity (psychological) , standard deviation , computer vision , mathematics , statistics , psychology , developmental psychology
A computer vision-based non-destructive pineapple maturity level identification system has been realized. This research was conducted to create a system capable of identifying six indexes of pineapple maturity level. An artificial neural network is used as a classifier for the level of maturity pineapples. Artificial neural network input is a statistical parameter consisting of mean, standard deviation, variance, kurtosis, and skewness of RGB and HSV color models pineapple images. Statistical parameters of the color model with a Pearson correlation value greater than 0.5 were used to characterize pineapple images. A total of 360 pineapple images were used in the training process with a percentage of 75% of training data and 25% of validation data. An image segmentation process is applied to separate the pineapple image from the image background. The result of this research is a pineapple maturity level identification system consisting of software and hardware which is able to identify six indexes of pineapple maturity level with average accuracy value of 98,4%.