KAMADHENU- Microcontroller Based Health and Feed Intake Monitoring System for Dairy Cows

Authors

  • Asha S. Manek T. John Institute of Technology, Bangalore, INDIA
  • Sharon Christa MIT ADT University, Pune, INDIA
  • Vineeta Apex Institute of Technology, Chandigarh University, Punjab, INDIA
  • Anuradha Kondelwar Priyadarshini College of Engineering, Nagpur INDIA
  • Shruti Vashisht Manav Rachna International University, Faridabad, INDIA
  • Geeta Tripathi GNITC, Hyderabad, INDIA

Keywords:

Cattle behavior, Feed Intake, Node Microcontroller, Sensors, Precision Cattle Farming, Cattle Monitoring

Abstract

The surroundings of cattle and the amount of grain that each cow consumes both affect the health of the cows and their ability to produce milk. Monitoring the feed intake condition of cows, together with cattle movement, are the two factors that make it difficult to identify and treat diseased cows early. Dairy farms aren't automated in places like India. Additionally, the cattle are scarce and graze in open areas. Tracking the movement of the cattle is therefore crucial. The goal of this study is to create an automated KAMADHENU neck band which monitors feed intake, cattle behavior, global positioning and real time location of dairy farm cows.  The KAMADHENU automated monitoring system analyses the sensor data for the cows' temperature and pulse before determining whether the cows are healthy or not.  The KAMADHENU measures cow’s behavior and records its activities under farm conditions with the advantage of small size, light weight and low power consumption sensor based electronic device. The overall performance of the proposed system is based on Node microcontroller and sensors. The KAMADHENU neck band performs better with accuracy greater than 90%.  Further, in context to the Indian scenario the proposed system represents a better performance over existing systems and evaluation methods.

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References

A. Minhas “Cattle population in India from 2016 to 2022, with an estimate for 2023” Link: https://www.statista.com/statistics/1181408/india-cattle-population/ Accessed on: January 22nd, 2023.

Chen, Z., Cheng, X., Wang, X. and Han, M., 2020, December. Recognition method of dairy cow feeding behavior based on convolutional neural network. In Journal of Physics: Conference Series (Vol. 1693, No. 1, p. 012166). IOP Publishing.

Mr. Kunja Bihari Swain and Satyasopan Mahato has given an idea about Health monitoring system using zigbee module in his paper “Cattle health monitoring system using Arduino and LabVIEW for early detection of diseases” published in 2017 IEEE 3rd International Conference on Sensing, Signal Processing and Security (ICSSS).

Mengmeng Wang. Exchange of New Technologies to Promote New Products and Improve the Development of the Dairy[J]. China dairy. 141(36), (2013).

Orpin, P.G. and Esslemont, R.J., 2010. Culling and wastage in dairy herds: an update on incidence and economic impact in dairy herds in the UK. Cattle Practice, 18(3), pp.163-172.

Firk, R., Stamer, E., Junge, W. and Krieter, J., 2002. Automation of oestrus detection in dairy cows: a review. Livestock Production Science, 75(3), pp.219-232.

Pollock, J.G., Gordon, A.W., Huson, K.M. and McConnell, D.A., 2022. The Effect of Frequency of Fresh Pasture Allocation on the Feeding Behaviour of High Production Dairy Cows. Animals, 12(3), p.243.

Cabezas, J., Yubero, R., Visitación, B., Navarro-García, J., Algar, M.J., Cano, E.L. and Ortega, F., 2022. Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection. Entropy, 24(3), p.336.

Hart, L., Dickhoefer, U., Paulenz, E. and Umstaetter, C., 2022. Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows. Sensors, 22(3), p.968.

Mayer, Kevin, Keith Ellis, and Ken Taylor. "Cattle health monitoring using wireless sensor networks." In Proceedings of the Communication and Computer Networks Conference (CCN 2004), pp. 8-10. ACTA Press, 2004.

Chapinal, N., Veira, D.M., Weary, D.M. and Von Keyserlingk, M.A.G., 2007. Validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. Journal of dairy science, 90(12), pp.5732-5736.

Kwong, K.H., Wu, T.T., Goh, H.G., Sasloglou, K., Stephen, B., Glover, I., Shen, C., Du, W., Michie, C. and Andonovic, I., 2012. Practical considerations for wireless sensor networks in cattle monitoring applications. Computers and Electronics in Agriculture, 81, pp.33-44.

Orpin, P.G. and Esslemont, R.J., 2010. Culling and wastage in dairy herds: an update on incidence and economic impact in dairy herds in the UK. Cattle Practice, 18(3), pp.163-172.

Firk, R., Stamer, E., Junge, W. and Krieter, J., 2002. Automation of oestrus detection in dairy cows: a review. Livestock Production Science, 75(3), pp.219-232.

Suseendran, G. and Balaganesh, D., 2021. Smart cattle health monitoring system using IoT sensors. Materials Today: Proceedings.

Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A. and Skarin, A., 2021. Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture. Animals, 11(3), p.829.

Ashok Kumar, L. ., Jebarani, M. R. E. ., & Gokula Krishnan, V. . (2023). Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 86–93. https://doi.org/10.17762/ijritcc.v11i2.6132

Jóhann, Þorvaldsson, Koskinen, P., Meer, P. van der, Steiner, M., & Keller, T. Improving Graduation Rates in Engineering Programs Using Machine Learning. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/110

Mandal, D., Shukla, A., Ghosh, A., Gupta, A., & Dhabliya, D. (2022). Molecular dynamics simulation for serial and parallel computation using leaf frog algorithm. Paper presented at the PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, 552-557. doi:10.1109/PDGC56933.2022.10053161 Retrieved from www.scopus.com

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Published

16.07.2023

How to Cite

Manek, A. S. ., Christa, S. ., Vineeta, Kondelwar, A. ., Vashisht, S. ., & Tripathi, G. . (2023). KAMADHENU- Microcontroller Based Health and Feed Intake Monitoring System for Dairy Cows. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 988–997. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3353

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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