The Identification of Beef and Pork Using Neural Network Based on Texture Features

Abstract

The actual problem that frequently happens related to meat sales at conventional markets is the manipulation of pork and beef. It can happen as both visual textures bear resemblances. Texture is a crucial part of an object. In image processing, textures can be used for classification, recognition or prediction of an image. This paper offers the Minimum Overlap Probability - Neural Network method for the identification of digital image features of pork and beef.. Minimum Overlap Probability was employed to select features of the strongest characteristics, whilst Neural Network is used for training and classification. Based on the test results, the strongest features are maximum probability, contrast, sum average, autocorrelation, and energy and entropy sum. Based on MOP-NN Model test result, the digital image identification of beef and pork has performance with an accuracy of 96% on 400 images of sample data.

Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data

“…Ada juga penelitian yang menggunakan kombinasi ekstraksi fitur berdasarkan tekstur dan warna menggunakan fitur HSV dan LOOP dan berhasil mencapai akurasi 100% untuk semua kategori pengukuran performa yaitu accuracy, recall, dan precision [7]. Serta penelitian menggunakan Neural Network dan fitur tekstur berhasil mencapai akurasi sebesar 90% dengan menggunakan 400 data gambar [8].…”