• Korean Journal of Organic Agriculture
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• v.19 no.spc
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• pp.186-189
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• 2011

By surveying the 70 composting plants in Gyeonggi Province, the total commercial production of livestock faeces composts (LFCs) in 2008 was estimated to be about 480,000 Mg year-1 and they were manufactured mainly by using both mechanical mixer and bottom air blower. LFCs were composed mainly of chicken faeces 29.2%, pig+chicken faeces 23.1%, pig faeces 20.0%, livestock faeces+oil cake 12.3%, pig+chicken+cattle faeces 10.8% and pig+cattle faeces 4.6%. On the basis of the current official standard which was revised on March 2010, 11 composts out of surveyed 76 ones did not meet the LFCs quality standard (LQS) due to inadequate content of water (5), OM/N (1), NaCl (2) and Zn (3). The OM/N declined by adding chicken faeces and oil cake, while Ca content increased by the addition of chicken faeces and NaCl increased by adding cattle faeces.

• Korean Journal of Organic Agriculture
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• v.19 no.spc
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• pp.182-185
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• 2011

Soil $NH_4$-N content became higher in proportion to the increase in the urea application rate, while in livestock faeces compost (LFC) plots, it became lower than in urea plots and had no significant difference statistically among LFC plots. There was a close relationship between phosphate fertilization rate and the increment of soil available phosphate content after experiment resulting y=0.1788x-6.169 ($R^2=0.9425$) when applied fused superphosphate fertilizer, and y=0.0662x-2.689 ($R^2=0.9315$) when applied LFCs by the same amounts of phosphate (x: phosphate application, kg $ha^{-1}$, y: increment in soil available phosphate content, mg $kg^{-1}$. Plant height, number of stems, nutrients uptake by rice and rice yield showed higher levels in N 100, 150% application plots of chemical fertilizers, while every LFC plots exhibited lower values and no significant difference among them. Relative nitrogen fertilization efficiencies of LFCs compared to urea was 12.3% for cattle faeces compost (CaFC), 8.8 for swine faeces compost (SwFC) and 24.6 for chicken faeces compost (ChFC), respectively.

• Journal of Biosystems Engineering
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• v.29 no.4
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.

• Journal of Embryo Transfer
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• v.28 no.4
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• pp.337-342
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• 2013

Salmonellosis is one of the life-threating diseases of goat in Bangladesh. Therefore, the present study was designed to study the prevalence of Salmonellosis, and isolation and characterizations of the Salmonella spp. from apparently healthy and diarrheic goat. A total of 47 faces samples were collected from selected place and cultured onto different prescribed medium to isolate it. In this study, 12.76% (6/47) samples were found to be positive for Salmonella spp. During culture on SS agar medium, all of the Salmonella isolates produced round, smooth, opaque, translucent and black color colonies on SS agar media. All of the isolated Salmonella spp. fermented dextrose, maltose and mannitol with production of acid and gas but did not ferment sucrose and lactose. However, these isolates had showed Indole and Voges-Proskauer test negative, Methyl-Red test positive. All of these isolates were subjected to rapid plate agglutination test with polyvalent "O" (Poly 'O') and polyvalent "H" (poly 'H') antisera where positive agglutination were observed. They were highly sensitive to ciprofloxacin, spiramycin and gentamycin; moderately sensitive to oxytetracyline, streptomycin and amoxicillin; less sensitive to sulphamethoxazole and resistant to penicillin-G. Based on the present findings, it may be concluded that the investigated Salmonella spp. from goats might be S. typhimurium, S. enteritidis, S. brandenburg, S. salford, S. newbrunswick, S. newport or S. dublin. Further study will be needed, therefore it requires further characterization using other serological and molecular techniques.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.493-494
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• 2022

The development of livestock faces intensive farming results in a rising need for recognition of individual animals such as cows and pigs is related to high traceability. In this paper, we present a non-invasive biometrics systematic approach based on the deep-learning classification model to pig face identification. Firstly, in our systematic method, we build a ROS data collection system block to collect 10 pig face data images. Secondly, we proposed a preprocessing block in that we utilize the SSIM method to filter some images of collected images that have high similarity. Thirdly, we employ the improved image classification model of CNN (ViT), which uses the finetuning and pretraining technique to recognize the individual pig face. Finally, our proposed method achieves the accuracy about 98.66%.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.6
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• pp.868-872
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• 2005

Individual management of the animal is the first step towards reaching the goal of precision livestock farming that aids animal welfare. Accurate recognition of each individual animal is important for precise management. Electronic identification of cattle, usually referred to as RFID (Radio Frequency Identification), has many advantages for farm management. In practice, however, RFID implementations can cause several problems. Reading speed and distance must be optimized for specific applications. Image processing is more effective than RFID for the development of precision farming system in livestock. Therefore, the aim of this paper is to attempt the identification of cattle by using image processing. The majority of the research on the identification of cattle by using image processing has been for the black-and-white patterns of the Holstein. But, native Japanese and Korean cattle do not have a consistent pattern on the body, so that identification by pattern is impossible. This research aims to identify to Japanese black cattle, which does not have a black-white pattern on the body, by using image processing and a neural network algorithm. 12 Japanese black cattle were tested. Values of input parameter were calculated by using the face image values of 12 cows. The face was identified by the associate neural memory algorithm, and the algorithm was verified by the transformed face image, for example, of brightness, distortion, noise and angle. As a result, there was difference due to a transformation ratio of the brightness, distortion, noise, and angle. The algorithm could identify 100% in the range from -30 to +30 degrees of brightness, -20 to +40 degrees of distortion, 0 to 60% of noise and -20 to +30 degree of angle transformed images.

• Journal of Animal Environmental Science
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• v.13 no.1
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• pp.35-44
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• 2007

The effects of microbial feedstuff additives on feed conversion rate and physical and chemical characteristics of excreta in growing pigs were investigated. Three different products (A, B and C) were compared. Microbial population tests showed B contained higher numbers of total bacteria, Lactobacillus spp. and yeasts. The amylase activity of B was also higher than that of A and C. The daily feed intake rates fer control, A, B and C were 2.06, 2.13, 2.17 and 2.34 kg, respectively. Pigs feed product C had the highest liveweight gain(2.89 kg). However, the results of feed conversion rate were not significantly different between treatments. Amount of faces excreted for control, A, B and C was 1.18, 1,19, 1.23 and 1.32 kg, respectively. Urine volume for control, A, B, and C was 1.91, 1.80, 2.19 and 2.31 kg respectively. Moisture content, T-N, $P_2O_5$ and $K_2O$ in pig manure were not significantly different between treatments. The range of BOD values was 63,453 to $73,758mg/\ell$ for faeces, and 5,678 to $7,428mg/\ell$, for urine. SS values of solid and liquid excreta ranged from 142,200 to 176,000 and from 710 to $1,025mg/\ell$, respectively.