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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 충남대학교 농업과학연구소 Korean Journal of Agricultural Science Korean Journal of Agricultural Science Vol.47 No.4
- 발행연도
- 2020.12
- 수록면
- 1,109 - 1,122 (14page)
이용수
초록· 키워드
오류제보하기
The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.
목차
Abstract
Introduction
Materials and Methods
Results and Discussion
Conclusion
References
참고문헌 (30)
참고문헌 신청
Barbon AP / 2017 / Development of a flexible computer vision system for marbling classification / Computers and Electronics in Agriculture 142:536~544
Barbon DF / 2013 / NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen–thawed porcine longissimus dorsi muscles / Innovative Food Science & Emerging Technologies 18:226~236
Dyrmann M / 2016 / Plant species classification using deep convolutional neural network / Biosystems Engineering 151:72~80
Farrow RL / 2009 / An exploratory observational study to develop an improved method for quantifying beef carcass salable meat yield / Meat Science 82:143~150
Glorot X / 2011 / Deep sparse rectifier neural networks / Proceedings of the fourteenth international conference on artificial intelligence and statistics :315~323
Barbon DF / 2013 / NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen–thawed porcine longissimus dorsi muscles / Innovative Food Science & Emerging Technologies 18:226~236
Dyrmann M / 2016 / Plant species classification using deep convolutional neural network / Biosystems Engineering 151:72~80
Farrow RL / 2009 / An exploratory observational study to develop an improved method for quantifying beef carcass salable meat yield / Meat Science 82:143~150
Glorot X / 2011 / Deep sparse rectifier neural networks / Proceedings of the fourteenth international conference on artificial intelligence and statistics :315~323
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