Open AccessPig Breeds Classification using Neuro-Statistic Model
Author(s) -
Satyendra Nath Mandal,
Sanket Dan,
Pritam Ghosh,
Subhranil Mustafi,
Kunal Roy,
Kaushik Mukherjee,
Dilip Kumar Hajra,
S. Banik
Publication year - 2019
Publication title -
science and technology journal
Language(s) - English
Resource type - Journals
ISSN - 2321-3388
DOI - 10.22232/stj.2019.07.02.10
Subject(s) - pattern recognition (psychology) , statistic , artificial intelligence , artificial neural network , segmentation , histogram , computer science , breed , standard deviation , statistics , mathematics , image (mathematics) , biology , genetics
Image classification using fully connected neural network is not efficient due to huge number of parameters in each layer. In this paper, we propose a Neuro-Statistic model for classification of five different pig breeds from pig images. The model consists of four sub modules which work together as a layered structure. We captured multiple individual pig images of five different pig breeds from different organized farms to conduct this research, segmented the captured pig images using hue based segmentation algorithm and then calculated the statistical properties like entropy, standard deviation, variance, mean, median, mode and color properties like H.S.V from the content of the individual segmented images. We fed all the extracted properties into Neural Network for Pig Breed (NNPB) to perform pig breed prediction with the classification module and analyzed the best performance, regression error plot, Error histogram and training state of NNPB. The performance of NNPB network was accepted based on error analysis and finally, we used the trained model to predict the breed of 50 pig images and achieved the prediction accuracy of 90%.