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가축의 생체중은 건강 및 사육 환경 관리에 중요한 정보이고 이를 통해 최적 사료량이나 출하 시기 등을 결정하게 된다. 일반적으로 가축의 무게를 측정할 때 체중계를 이용하지만, 체중계를 이용한 가축 무게를 측정하는데 상당한 인력과 시간이 필요하고 성장 단계별 측정이 어려워 사료급이량 조절 등의 효과적인 사육 방법이 적용되지 못하는 단점이 있다. 본 연구는 축산 양돈 분야에 영상 및 이미지 데이터를 수집, 분석, 학습, 예측 등을 통해 포유자돈, 이유자돈, 육성돈, 비육돈 구간별 체중 측정에 관한 연구와 함께 정확도를 높이고자 하였다. 이를 위해 파이토치(pytorch), YOLO(you only look once) 5 모델, 사이킷런(scikit learn) 라이브러리를 사용하여 학습시킨 결과, 실제치(actual)와 예측치(prediction) 그래프에서 RMSE(root mean square error) 0.4%와 MAPE(mean absolute percentage error) 0.2%로 유사한 흐름을 확인할 수 있다. 이는 양돈 분야의 포유자돈, 이유자돈, 육성돈, 비육돈 구간에서 활용할 수 있으며 다각도로 학습된 이미지 및 영상 데이터와 실제 측정된 체중 데이터를 바탕으로 지속적인 정확도 향상이 가능하고 향후 영상판독을 통해 돼지의 부유별 생산량에 대한 예측으로 효율적인 사육관리가 가능할 것으로 기대된다.

The live weight of livestock is important information for managing their health and housing conditions, and it can be used to determine the optimal amount of feed and the timing of shipment. In general, it takes a lot of human resources and time to weigh livestock using a scale, and it is not easy to measure each stage of growth, which prevents effective breeding methods such as feeding amount control from being applied. In this paper, we aims to improve the accuracy of weight measurement of piglets, weaned pigs, nursery pigs, and fattening pigs by collecting, analyzing, learning, and predicting video and image data in animal husbandry and pig farming. For this purpose, we trained using Pytorch, YOLO(you only look once) 5 model, and Scikit Learn library and found that the actual and prediction graphs showed a similar flow with a of RMSE(root mean square error) 0.4%. and MAPE(mean absolute percentage error) 0.2%. It can be utilized in the mammalian pig, weaning pig, nursery pig, and fattening pig sections. The accuracy is expected to be continuously improved based on variously trained image and video data and actual measured weight data. It is expected that efficient breeding management will be possible by predicting the production of pigs by part through video reading in the future.

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권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 목차
Boot storm reduction through artificial intelligence driven system in virtual desktop infrastructure Heejin Lee, Taeyoung Kim p. 1-9

Bit-width aware generator and intermediate layer knowledge distillation using channel-wise attention for generative data-free quantization Jae-Yong Baek, Du-Hwan Hur, Deok-Woong Kim, Yong-Sang Yoo, Hyuk-Jin Shin, Dae-Hyeon Park, Seung-Hwan Bae p. 11-20

(A) development of shoes cleaner control system using Raspberry Pi Deukchang Hyun p. 21-32

Pig image learning for improving weight measurement accuracy Jonghee Lee, Seonwoo Park, Gipou Nam, Jinwook Jang, Sungho Lee p. 33-40

Research on local and global infrared image pre-processing methods for deep learning based guided weapon target detection Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon, Seung-Hwan Bae p. 41-51

Analysis of key factors in corporate adoption of generative artificial intelligence based on the UTAUT2 model Yongfeng Hu, Haojie Jiang, Chi Gong p. 53-71

KOSPI index prediction using topic modeling and LSTM Jin-Hyeon Joo, Geun-Duk Park p. 73-80

Design of a question-answering system based on RAG model for domestic companies Gwang-Wu Yi, Soo Kyun Kim p. 81-88

Improving accuracy of chapter-level lecture video recommendation system using keyword cluster-based graph neural networks Purevsuren Chimeddorj, Doohyun Kim p. 89-98

Analysis of trends in information security using LDA topic modeling Se Young Yuk, Hyun-Jong Cha, Ah Reum Kang p. 99-107

Analysis of security vulnerabilities and personal resource exposure risks in Overleaf Suzi Kim, Jiyeon Lee p. 109-115

(A) study on the application of legal design methodology for commercialization of security tokens Sangyub Han, Hokyoung Ryu p. 117-128

(A) study on the required characteristics of collaborative online platform for social enterprises Sun-Hwa Lee, Jong-Soo Yoon p. 129-137

(A) study on the impact of Chinese online customer reviews on consumer purchase behavior in online education platforms Shuang Guo, Yumi Kim p. 139-148

Design of multi-sensor system for comprehensive indoor air quality monitoring TaeHeon Kim, SungYeup Kim, Yoosin Kim, Min Hong p. 149-158

(The) effects of the leader‘s transactional and transformational leadership on life satisfaction for the 119 rescue workers Byung-Jun Cho, Il-Soon Choi, Tae-Hyun Lee p. 159-167

Reflections on the possibility of replacing the registration system with a blockchain system Jong-Ryeol Park, Sang-Ouk Noe p. 169-179

(The) effect of lab classes satisfaction of culinary-related majors on academic stress and class participation in local colleges Pyoung-Sim Park p. 181-190

(A) study on efficient user management system of combat system Hee-Soo Kim p. 191-198

(The) mechanism of China's green financial policy on renewable energy industry Pei-gen Li, Zhuo Li p. 199-207

Research on the impact of digital music products on the development of regional tourism economy Jun-Shu Liu p. 209-219

Estimation and comparison of regional innovative human capital in China Sangwook Kim p. 221-229

Analysis of perceptions and needs of generative AI for work-related use in elementary and secondary education Hye Jin Yun, Kwihoon Kim p. 231-243

참고문헌 (17건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Statistics Korea press release, “Changes in the livestock industry based on statistics”, 2020. 미소장
2 Halachmi, I. Guarino, M.; Bewley, J. Pastell, M. “Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production”, Annu. Rev. Anim. Biosci. Vol. 7, pp. 403–425, 2019, DOI:10.1146/annurev-animal-020518-114851 미소장
3 Kollis, K. Phang, C.S. Banhazi, T.M. Searle, S.J. “Weight Estimation Using Image Analysis and Statistical Modelling: A Preliminary Study”, Appl. Eng. Agric., Vol 23, No. 1, pp. 91–96, 2007, DOI:10.13031/2013.22332 미소장
4 Dickinson, R.A. Morton, J.M. Begs, D.S. Anderson, G.A. Pyman, M.F. Mansell, P.D. Blackwood, C.B. “An Automated Walk-over Weighing System as a Tool for Measuring Liveweight Change in Lactating Dairy Cows”, J. Dairy Sci., Vol. 96, No. 7, pp. 4477–4486, 2013, DOI:10.3168/jds.2012-6522 미소장
5 Jang, J. Lee, J. Nam, G. Lee, S. “System Design of Optimal Pig Shipment Schedule through Prediction Model”, Agriculture, Vol. 13, No. 8, pp. 1520, 2023, DOI:10.3390/agriculture13081520 미소장
6 Groesbeck, C N. Lawrence, K R. Young, M G. Goodband, Robert D. DeRouchey, Joel M. Tokach, Michael D. Nelssen, Jim L and Dritz, Steven S. "Using heart girth to determine weight in finishing pigs", Kansas Agricultural Experiment Station Research Reports, Vol. 0: No. 10. pp. 166-168, 2002, DOI:10.4148/2378-5977.6739 미소장
7 M. Kashiha, C. Bahr, S. Ott, C. Moons, T. Niewold, F. Odberg, D. Berckmans, “Weight Estimation of Pigs Using Top-view Image Processing”, in Proceeding International Conference Image Ananlysis and Recongnition, Vol. 8814, pp. 496-503, 2014, DOI:10.1007/978-3-319-11758-4_54 미소장
8 M. Kashiha, C. Bahr, S. Ott, C. Moons, T. Niewold, F.Odberg, D. Berckmans, “Automatic Weight Estimation of Individual Pigs Using Image Analysis”, Computers and Electronics in Agriculture, Vol. 107, pp. 38-44, 2014, DOI:10.1016/j.compag.2014.06.003 미소장
9 M. Lu. T. Norton, A. Youssef, N. Radojkovic, A. Fernandez, D. Berckmans, “Extracting Boby Surface Dimensions from Top-view Images of Pigs", International Journal of Agricultureal and Biological Engineering, Vol. 11, No. 5, pp. 182-1191, 2018, DOI:10.25165/j.ijabe.20181105.4054 미소장
10 A. Pezzuoloa, M. Guarinob, L. Sartoria, L. Gonzalezc, F.Marinelloa, “On-barn Pig Weight Estimaton Based on Body Measurments by a Kinect v1 Depth Camera”, Computers and Electronics in Agriculture, Vol. 148, pp.29-36, 2018, DOI:10.1016/j.compag. 2018.03.003 미소장
11 Ke Wang, Hao Guo, Qin Ma, Wei Su, Luochao Chen, Dehai Zhu, “A portable and automatic Xtion-based measurement system for pig body size", Computers and Electronics in Agriculture, Vol. 148, pp. 291-298, May, 2018, DOI:10.1016/j.compag.2018.03.018 미소장
12 Apirachai Wongsriworaphon, Banchar Arnonkijpanich and Supachai Pathumnakul "An approach based on digital image analysis to estimate the live weights of pigs in farm environments", Computers and Electronics in Agriculture, Vol. 115, pp. 26-33, July, 2015, DOI:10.1016/j.compag.2015.05.004 미소장
13 Y. Cang, H. He, Y. Qiao, “An Intelligent Pig Weights Estimate Method Based on Deep Learning in Sow Stall Environments”, IEEE Access, Vol. 7, pp. 164867-164875, November, 2019, DOI:10.1109/ACCESS.2019.2953099 미소장
14 C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Deep Learning on Point sets for 3d Classification and Segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652-680, 2017, DOI:10.48550/arXiv.1612.00593 미소장
15 Yao Liu, Jie Zhou, Yifan Bian, Taishan Wang, Hongxiang Xue and Longshen Liu, "Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data", Animals 2024, 14(7), DOI:10.3390/ani14071046 미소장
16 Junbin Liu, Deqin Xiao, Youfu Liu and Yigui Huang, "A Pig Mass Estimation Model Based on Deep Learning without Constraint", Animals 2023, 13(8), DOI:/10.3390/ani13081376 미소장
17 WISESTONE, “test report(No:2023-574-VSW-R)”, 2024.1.5. 미소장

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