Sistem Identifikasi Tingkat Kematangan Buah Nanas Secara Non-Destruktif Berbasis Computer Vision

Nevalen Aginda Prasetyo; Arif Surtono; Junaidi Junaidi; Gurum Ahmad Pauzi

doi:10.23960/jemit.v2i1.26

Authors

Nevalen Aginda Prasetyo Universitas Lampung
Arif Surtono Departement of Physics, University of Lampung, Indonesia, 35141
Junaidi Junaidi Departement of Physics, University of Lampung, Indonesia, 35141
Gurum Ahmad Pauzi Departement of Physics, University of Lampung, Indonesia, 35141

DOI:

https://doi.org/10.23960/jemit.v2i1.26

Keywords:

Artificial neural networks, Computer Vision, Pineapple

Abstract

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%.

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