A muzzle-based self-enhancing cow recognition method using YOLO and LLM
Abstract
In the modern era, using the technology of image processing, people are being
facilitated in many sectors. Therefore, we have decided to do research on cow
recognition using the methods of image processing. Because cows are pretty
essential for human beings, and people from the entire world are being facilitated
by cows. Because of that reason, many technologies have been invented till now
for the cow. Here we have presented an approach to identify the cow. Like the
fingerprint of a human, the cow muzzle is also unique. Due to this reason, we
can recognize the cow based on the muzzle. To recognize muzzles, we have used
Yolo and the Structural Similarity Index (SSI). Image processing is a sector of
Artificial Intelligence and data is quite important to train models. Moreover, to
train a model for image processing, it is needed to manage a high-configuration
computer. But there are many regions in the world where people hardly get such
powerful machines. To mitigate this issue, we have introduced a unique method
where we do not need a large amount of data for the image processing task. To
eliminate the necessity of collecting a huge amount of data, we included a self-
enhancing feature, which helps the model to predict accurately and also helps
to improve performance by increasing the amount of data. However, without
a large amount of data, it will be possible to predict appropriately because of
the presence of self-enhancing features. This certain program helps the model
to improve its capability for delivering faster responses. LLM is responsible to
add the self-enhancing feature. On the other hand, to find out the most suitable
Yolo model for our research, we have trained data with three different versions
of Yolo: Yolo v7, v8, and v9. We evaluated the performances of these three
versions in terms of Precision, Recall, mAP50, and mAP50-95 and found Yolo
v9 to be the best among them. It excels with a precision of 0.391, a recall of
0.916, a mAP50 of 0.559, and a mAP50-95 of 0.307.
Keywords
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AnimalSmart.org, “Cattle,” [Online]. Available: https://animalsmart.org/species/
cattle. [Accessed: August 25, 2024].
R. Grant and W. H. Miner, “Economic benefits of improved cow comfort,” St. Charles, MO: Novus
International, 2015.
J. Trivedi and B. Kunal Soni, “A Study on Cattle Insurance in Villages of Gandhinagar District,” Business
Perspectives and Research, vol. 2, no. 2, pp. 25-36, 2014.
Business Companion, “Cattle identification,” [Online]. Available: https://www.
businesscompanion.info/en/quick-guides/animals-and-agriculture/
cattle-identification. [Accessed: August 25, 2024].
M. J. Chen and A. C. Bovik, “Fast structural similarity index algorithm,” Journal of Real-Time Image
Processing, vol. 6, pp. 281-287, 2011.
TechTarget, “Large language model (LLM),” [Online]. Available: https://www.techtarget.
com/whatis/definition/large-language-model-LLM. [Accessed: August 25, 2024].
T. T. Zin, S. Misawa, M. Z. Pwint, S. Thant, P. T. Seint, K. Sumi, and K. Yoshida, “Cow identification sys-
tem using ear tag recognition,” in 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
(LifeTech), pp. 65-66, March 2020.
Y. Lu, X. He, Y. Wen, and P. S. Wang, “A new cow identification system based on iris analysis and
recognition,” International Journal of Biometrics, vol. 6, no. 1, pp. 18-32, 2014.
J. Jingqiu, W. Zhihai, G. Ronghua, and W. Huarui, “Cow behavior recognition based on image analysis
and activities,” International Journal of Agricultural and Biological Engineering, vol. 10, no. 3, pp. 165-
, 2017.
L. Yao, Z. Hu, C. Liu, H. Liu, Y. Kuang, and Y. Gao, “Cow face detection and recognition based on au-
tomatic feature extraction algorithm,” in Proceedings of the ACM Turing Celebration Conference-China,
pp. 1-5, May 2019.
S. Li, L. Fu, Y. Sun, Y. Mu, L. Chen, J. Li, and H. Gong, “Individual dairy cow identification based on
lightweight convolutional neural network,” PLOS ONE, vol. 16, no. 11, e0260510, 2021.
T. T. Zin, C. N. Phyo, P. Tin, H. Hama, and I. Kobayashi, “Image technology based cow identification sys-
tem using deep learning,” in Proceedings of the International Multiconference of Engineers and Computer
Scientists, vol. 1, pp. 236-247, March 2018.
S. Kumar, S. K. Singh, T. Dutta, and H. P. Gupta, “A fast cattle recognition system using smart devices,”
in Proceedings of the 24th ACM International Conference on Multimedia, pp. 742-743, October 2016.
R. W. Bello, A. Z. Talib, A. S. A. Mohamed, D. A. Olubummo, and F. N. Otobo, “Image-based individual
cow recognition using body patterns,” Image, vol. 11, no. 3, pp. 92-98, 2020.
T. T. Zin, M. Z. Pwint, P. T. Seint, S. Thant, S. Misawa, K. Sumi, and K. Yoshida, “Automatic cow location
tracking system using ear tag visual analysis,” Sensors, vol. 20, no. 12, 3564, 2020.
R. W. Bello, A. Z. H. Talıb, and A. S. A. B. Mohamed, “Deep learning-based architectures for recognition
of cow using cow nose image pattern,” Gazi University Journal of Science, vol. 33, no. 3, pp. 831-844,
S. Kumar, S. Tiwari, and S. K. Singh, “Face recognition for cattle,” in 2015 Third International Confer-
ence on Image Information Processing (ICIIP), pp. 65-72, December 2015.
Z. Wang, Z. Hua, Y. Wen, S. Zhang, X. Xu, and H. Song, “E-YOLO: Recognition of estrus cow based on
improved YOLOv8n model,” Expert Systems with Applications, vol. 238, 122212, 2024.
Int J Inf & Commun Technol, Vol. , No. , : 1–1x
Int J Inf & Commun Technol ISSN: ❒ 13
K. Li, D. Fan, H. Wu, and A. Zhao, “A new dataset for video-based cow behavior recognition,” Scientific
Reports, vol. 14, no. 1, 18702, 2024.
Z. Weng, F. Meng, S. Liu, Y. Zhang, Z. Zheng, and C. Gong, “Cattle face recognition based on a Two-
Branch convolutional neural network,” Computers and Electronics in Agriculture, vol. 196, 106871, 2022.
Siddiqua, R., Rahman, S., Uddin, J. (2021). A deep learning-based dengue mosquito detection method
using faster R-CNN and image processing techniques. Annals of Emerging Technologies in Computing
(AETiC), 5(3), 11-23.
Khondaker, A., Khandaker, A., Uddin, J. (2020). Computer vision-based early fire detection using en-
hanced chromatic segmentation and optical flow analysis technique. Int. Arab J. Inf. Technol., 17(6),
-953.
IBM, “What are Large Language Models (LLMs)?” [Online]. Available: https://www.ibm.com/
topics/large-language-models. [Accessed: August 25, 2024].
S. Zhou, K. Cai, Y. Feng, X. Tang, H. Pang, J. He, and X. Shi, “An accurate detection model of Takifugu
rubripes using an improved YOLO-V7 network,” Journal of Marine Science and Engineering, vol. 11,
no. 5, 1051, 2023.
F. M. Talaat and H. ZainEldin, “An improved fire detection approach based on YOLO-v8 for smart cities,”
Neural Computing and Applications, vol. 35, no. 28, pp. 20939-20954, 2023.
Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: analysis,
applications, and prospects,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no.
, pp. 6999-7019, 2021.
M. A. R. Alif and M. Hussain, “YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and
their application in the agricultural domain,” arXiv preprint arXiv:2406.10139, 2024.
Google Docs,“Cow.v1i.yolov8.zip,”[Online].Available:
https://drive.google.com/file/d/1atJBLTbbUz02eClj_UEDVWOTJHxQ1NvK/
view?usp=drive_link
I. Bakurov, M. Buzzelli, R. Schettini, M. Castelli, and L. Vanneschi, “Structural similarity index (SSIM)
revisited: A data-driven approach,” Expert Systems with Applications, vol. 189, 116087, 2022.
D. Brunet, E. R. Vrscay, and Z. Wang, “On the mathematical properties of the structural similarity index,”
IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1488-1499, 2011.
R. Ong, “GPT-4O API tutorial: Getting started with OpenAI’s API,” DataCamp, [Online]. Avail-
able: https://www.datacamp.com/tutorial/gpt4o-api-openai-tutorial. [Ac-
cessed: August 20, 2024].
M. Buckland and F. Gey, “The relationship between recall and precision,” Journal of the American Society
for Information Science, vol. 45, no. 1, pp. 12-19, 1994.
M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly, “Assessing generative models via preci-
sion and recall,” in Advances in Neural Information Processing Systems, vol. 31, 2018.
X. Li, W. Wang, L. Sun, B. Hu, L. Zhu, and J. Zhang, “Deep learning-based defects detection of certain
aero-engine blades and vanes with DDSC-YOLOv5s,” Scientific Reports, vol. 12, no. 1, 13067, 2022.
Ultralytics, “YOLO performance metrics,” [Online]. Available: https://docs.ultralytics.
com/guides/yolo-performance-metrics. [Accessed: August 25, 2024].
DOI: http://doi.org/10.11591/ijict.v15i4.pp1861-1870
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