• Journal of Animal Science and Technology
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• v.63 no.2
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• pp.367-379
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• 2021

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.

• Korean Journal of Veterinary Research
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• v.41 no.3
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• pp.275-279
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• 2001

The classification of bony pieces which were excavated from Jongdali archaeological site in Jeju was studied. The total number of bone remains were 81 pieces, in which 31 pieces were classified into animal bones. The animal species consisted of Cervus spp., Sus scrofa, Bos taurus and Equus caballus. This finding suggests that the major fauna in this peroid(B.C. 100 - A.D. 100) is wild boar, deer, horse and cattle.

• Journal of the Korean Society of Earth Science Education
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• v.4 no.2
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• pp.151-165
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• 2011

Following a microgenetic design, this study was analysed the characteristic and the change of classification strategy that appear in college students' classification activity. The 4 tasks were developed for classification activity; a shell as a familiar real things, an animal fossil as a unfamiliar real things, a snow flake as a familiar picture cards and galaxy as a unfamiliar picture card. Achieved study to 6 college students who major in elementary education. Data were collected by interview with subjects, subject's classification schema, investigator's observation of subject's activity, and videotaped that record subject's subject classification process over an extended period of 6 times. Result proved in this study is as following. In the 6 times of the data collection procedures, a strategy F identifying concrete attribution of classification objects and a more detailed strategy X3 combining qualitative, spatial and dimensional attribution were found and more frequently used in both groups of college students which reported a classification process and did not report the process. While discovery and absorption of both a concrete classification strategy and a detailed classification strategy were rapidly developed in the reporting group, they were gradually developed in the non-reporting group. In addition to this, as the data collection procedures were progressing, the college students were familiar with change factors of classification tasks and in the case of pictures the classification strategy showed more desirable changes.

• Animal Bioscience
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• v.35 no.6
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• pp.791-803
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• 2022

Genome/gene-editing (GE) techniques, characterized by a low technological barrier, high efficiency, and broad application among organisms, are now being employed not only in medical science but also in agriculture/veterinary science. Different engineered CRISPR/Cas9s have been identified to expand the application of this technology. In pig production, GE is a precise new breeding technology (NBT), and promising outcomes in improving economic traits, such as growth, lean or healthy meat production, animal welfare, and disease resistance, have already been documented and reviewed. These promising achievements in porcine gene editing, including the Myostatin gene knockout (KO) in indigenous breeds to improve lean meat production, the uncoupling protein 1 (UCP1) gene knock-in to enhance piglet thermogenesis and survival under cold stress, the generation of GGTA1 and CMP-N-glycolylneuraminic acid hydroxylase (CMAH) gene double KO (dKO) pigs to produce healthy red meat, and the KO or deletion of exon 7 of the CD163 gene to confer resistance to porcine reproductive and respiratory syndrome virus infection, are described in the present article. Other related approaches for such purposes are also discussed. The current trend of global regulations or legislation for GE organisms is that they are exempted from classification as genetically modified organisms (GMOs) if no exogenes are integrated into the genome, according to product-based and not process-based methods. Moreover, an updated case study in the EU showed that current GMO legislation is not fit for purpose in term of NBTs, which contribute to the objectives of the EU's Green Deal and biodiversity strategies and even meet the United Nations' sustainable development goals for a more resilient and sustainable agri-food system. The GE pigs generated via NBT will be exempted from classification as GMOs, and their global valorization and commercialization can be foreseen.

• Food Science of Animal Resources
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• v.41 no.6
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• pp.936-949
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• 2021

This study discriminated fatty acid profile and flavor characteristics of Beijing You Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC) eggs. Fatty acid profile and flavor characteristics were analyzed to identify differences between BYC and DBYC eggs. Four classification algorithms were used to build classification models. Arachidic acid, oleic acid (OA), eicosatrienoic acid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA) and 35 volatile compounds had significant differences in fatty acids and volatile compounds by gas chromatography-mass spectrometry (GC-MS) (p<0.05). For fatty acid data, k-nearest neighbor (KNN) and support vector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS data failed in classification models. For electronic nose data, classification accuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree was all 100%. The overall results indicated that BYC and DBYC eggs could be discriminated based on electronic nose with suitable classification algorithms. This research compared the differentiation of the fatty acid profile and volatile compounds of various egg yolks. The results could be applied to evaluate egg nutrition and distinguish avian eggs.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• Journal of Animal Science and Technology
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• v.51 no.2
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• pp.97-104
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• 2009

Complement of test standard, evaluation methods and models are needed to improve national competitiveness and to exchange superior genetic resources through the comparison of genetic evaluation score among nations in dairy cattle. Therefore, this study was conducted for the application of international standard to Korea considering domestic circumstance by changing linear-classification test score system of 50 classes which is currently used in Korea to system of 9 classes which is used in advanced nations of dairy. 15,230 of holstein cow linear type records with first parity records for the fifteen linear type and one total score from 2001 to 2006 and pedigree data which were collected by the Korean Animal Improvement Association were used in this study. Population classified by 9 levels was more normal distributed than 50 levels. Correlation coefficients between 50 and 9 score system showed over 0.98 by each classification scheme. Therefore, the 50 point system can be substituted with 9 point system due to their highly positive correlation. However, scores in all traits were still very contingent on classifier under the 9 point system (p<0.001), and F values between foot angle and front teat attachment showed high fluctuation depending on classifier. It means that subjective opinions of classifier would influence on linear type score as ever even if class scheme transformed to system of 9 class. Therefore, the relevance of transformation to the 9 point system should be assessed after analyses about various environmental factors.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.9
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• pp.1203-1208
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• 2001

Linzhi Native Pig is a unique local breed recently discovered in the hinterland of Tibet. Its geological distribution, natural environment and ecological conditions have been explored. Using random sampling in typical colony of classification and standard animal-scientific and biogenetic techniques, we examined its contour features, size and weight, reproductive performances, carcass characters, meat quality, fresh-keeping features and the frequency distribution in the 19 structural gene loci encoding enzymes and proteins; according to folklores and Tibetan, Chinese and English history books, the materials and literature of Tibetan Studies, we have analyzed its origin and affirmed the fact that its products have been consumed as Tibetan medicine resources. Our findings make certain that Linzhi Native Pig holds great potential value in economy and culture.

• Animal cells and systems
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• v.8
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• pp.61-61
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• 2004

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.624-630
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• 2016

Many studies have reported the frequency and distribution of haplogroups among various cattle breeds for verification of their origins and genetic diversity. In this study, 318 complete sequences of the mtDNA control region from four Korean cattle breeds were used for haplogroup classification. 71 polymorphic sites and 66 haplotypes were found in these sequences. Consistent with the genetic patterns in previous reports, four haplogroups (T1, T2, T3, and T4) were identified in Korean cattle breeds. In addition, T1a, T3a, and T3b sub-haplogroups were classified. In the phylogenetic tree, each haplogroup formed an independent cluster. The frequencies of T3, T4, T1 (containing T1a), and T2 were 66%, 16%, 10%, and 8%, respectively. Especially, the T1 haplogroup contained only one haplotype and a sample. All four haplogroups were found in Chikso, Jeju black and Hanwoo. However, only the T3 and T4 haplogroups appeared in Heugu, and most Chikso populations showed a partial of four haplogroups. These results will be useful for stable conservation and efficient management of Korean cattle breeds.