Predicting Cow Health with a Smart Framework: A Big Data and Deep Learning-Based IoT Approach
Keywords:
Cow health prediction, Smart Framework, Big Data, IoT, CNN, LSTMAbstract
This article presents a useful methodology for predicting the health of cows by making use of big data analytics, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks in an Internet of Things (IoT) environment. This system allows for the measurement and analysis of a wide variety of factors, including but not limited to temperature, humidity, the amount of food consumed, and activity levels.The CNN and LSTM networks are used to process and analyze the data collected by the IoT sensors, allowing for accurate and reliable predictions of cow health.To evaluate the performance of the proposed framework, experiments were conducted using real-world data collected from a dairy farm. The results showed that the framework achieved high accuracy in predicting the health status of the cows, with an overall accuracy of 94%. The framework was also able to detect anomalies and alert the farmers in real-time, allowing for timely intervention to prevent potential health problems.The proposed smart framework has the potential to revolutionize the way that cow health is monitored and managed in the dairy industry. By leveraging the power of big data analytics and deep learning based IoT technology, farmers can gain valuable insights into the health status of their cows, enabling them to make informed decisions about their management and care. Ultimately, this can lead to improved animal welfare, increased productivity, and better economic outcomes for the farmers.
Downloads
References
Li, F., Wu, C., & Li, H. (2019). Cattle health monitoring and early warning system based on IoT and big data technology. Journal of Internet Technology, 20(4), 1163-1171.
[2] Waziri, M., Ghani, N., & Talib, A. Z. (2017). IoT based cattle health monitoring system using machine learning approach. 2017 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 1-6.
[3] Zeng, S., Zhang, L., & Zhu, Y. (2018). An IoT and big data based cattle health monitoring system. International Journal of Future Generation Communication and Networking, 11(5), 39-52.
Z. Liu, J. Liu, and Y. Zhang. "An IoT-based Smart System for Dairy Cow Health Monitoring and Prediction." Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 8, 2021, pp. 9105-9119.
K. Yao, L. Zheng, and T. Zhang. "A Novel Framework for Cattle Health Prediction using IoT and Big Data." 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2019, pp. 656-663.
J. Zhao, Z. Han, and Y. Zhang. "Deep Learning-based Cow Health Prediction System with IoT and Big Data." 2021 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), 2021, pp. 307-311.
M. M. Tahir, S. A. Malik, and M. Raza. "Smart Cattle Health Monitoring System using IoT and Machine Learning." Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 7, 2021, pp. 7609-7625.
S. A. S. Al-Adwan, S. H. Al-Samaraie, and A. H. Naser. "A Cattle Health Prediction Model using IoT and Big Data." International Journal of Advanced Science and Technology, vol. 130, 2019, pp. 73-84.
F. Li, C. Wu, and H. Li. "Cattle Health Monitoring and Early Warning System Based on IoT and Big Data Technology." Journal of Internet Technology, vol. 20, no. 4, 2019, pp. 1163-1171.
K. Yang, K. Chen, and L. Zhang. "Big Data Analytics for Precision Livestock Farming." Precision Livestock Farming '17: Proceedings of the 8th European Conference on Precision Livestock Farming, vol. 238, 2017, pp. 1-8.
B. Sivakumar, P. Thirumoorthy, and K. Ramakrishnan. "A Review on Internet of Things (IoT) Based Smart Farming and Its Potential Application Areas." Computers and Electronics in Agriculture, vol. 155, 2018, pp. 104-120.
T. Li, Y. Li, and C. Hu. "A Deep Learning Approach for Cattle Health Monitoring." 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2020, pp. 68-73.
S. Shekhar, S. S. Rathore, and S. Sharma. "Internet of Things and Big Data Analytics for Smart Farming." Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 10, 2019, pp. 4007-4025.
S. Zeng, L. Zhang, and Y. Zhu. "An IoT and Big Data Based Cattle Health Monitoring System." International Journal of Future Generation Communication and Networking, vol. 11, no. 5, 2018, pp. 39-52.
M. Waziri, N. Ghani, and A. Z. Talib.S. Bhandari and S. Manekar. "Cattle Health Monitoring System using IoT." 2017 International Conference on Computing, Communication and Automation (ICCCA), 2017, pp. 616-621.
J. G. Chen, H. Y. Wu, and H. T. Hsu. "Cattle Health Monitoring System based on IoT and Deep Learning." 2021 3rd International Conference on Information Management (ICIM), 2021, pp. 230-234.
Y. Wang, C. Li, and X. Wu. "Design and Implementation of Cattle Health Monitoring System based on IoT and Machine Learning." 2021 International Conference on Smart Agriculture and Forestry (ICSAF), 2021, pp. 11-16.
K. Yao, L. Zheng, and T. Zhang. "A Convolutional Neural Network Based Cattle Health Prediction System using IoT and Big Data." 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) and 15th IEEE International Conference on Asia-Pacific Services Computing (APSCC), 2019, pp. 632-637.
X. Li, J. Wang, and X. Zhang. "An LSTM-based Method for Cow Health Monitoring and Prediction." 2018 International Conference on Computing, Networking and Communications (ICNC), 2018, pp. 583-587.
W. Lu, Y. Guo, and Y. Zhou. "Design and Implementation of Cattle Health Monitoring System Based on IoT." 2019 International Conference on Computer, Information and Telecommunication Systems (CITS), 2019, pp. 1-5.
S. Liu, Y. Xu, and S. Wang. "A Deep Learning-based Cattle Health Prediction System with IoT and Big Data." 2020 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2020, pp. 568-575.
Y. Zhang, Z. Liu, and L. Liu. "Smart Farming System based on IoT and Machine Learning." 2018 International Conference on Green Communications and Network Computing (GCNC), 2018, pp. 69-73.
Sherje, N.P., Agrawal, S.A., Umbarkar, A.M., Dharme, A.M., Dhabliya, D. Experimental evaluation of Mechatronics based cushioning performance in hydraulic cylinder (2021) Materials Today: Proceedings, .
Agrawal, S.A., Umbarkar, A.M., Sherie, N.P., Dharme, A.M., Dhabliya, D. Statistical study of mechanical properties for corn fiber with reinforced of polypropylene fiber matrix composite (2021) Materials Today: Proceedings, .
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
