Precision Detection of Real-Time Conditions of Dairy Cows Using an Advanced Artificial Intelligence Hub
Source abstract
Abstract
One of the main challenges in the adoption of artificial intelligence-based tools, such as integrated decision support systems, is the complexities of their application. This study aimed to define the relevant parameters that can be used as indicators for real-time detection of heat stress and subclinical mastitis in dairy cows. Moreover, this study aimed to demonstrate the use of a developed data-mining hub as an artificial intelligence-based tool that integrates the defined relevant information (parameters or traits) in accurately identifying the condition of the cow. A comprehensive theoretical framework of the data-mining hub is demonstrated, the selection of the parameters that were used for the data-mining hub is listed, and the relevance of the traits is discussed. The practical application of the data-mining hub has shown that using 21 parameters instead of 13 and 8 parameters resulted in a high overall accuracy of detecting heat stress and subclinical mastitis in dairy cows with a high precision effect reflecting a low percentage of misclassifying the conditions of the dairy cows. This study has developed an innovative approach in which combined information from different independent data was used to accurately detect the health and wellness status of the dairy cows. It can also be implied that an artificial intelligence-based tool such as the proposed theoretical data-mining hub of dairy cows could maximize the use of continuously generated and underutilized data in farms, thus ultimately simplifying repetitive and difficult decision-making tasks in dairy farming.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Effects of Environmental Stressors on Livestock · Animal Behavior and Welfare Studies · Genetic and phenotypic traits in livestock
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
ThinkforBL Consultancy Services, Seoul 06236, Korea
Department of Animal Science, Chungbuk National University, Cheongju City 28644, Korea
School of IT Information and Control Engineering, Kunsan National University, Gunsan City 54150, Korea
Department of Biosystems Engineering, Chungbuk National University, Cheongju City 28644, Korea
References (58)
Show source-provided references
1. Accurate monitoring of the rumination behaviour of cattle using IMU signals from a mobile device
2. Original papers A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle10.1016/j.compag.2016.05.015 ↗
3. Acoustic monitoring system to quantify ingestive behavior of free-grazing cattle10.1016/j.compag.2011.01.009 ↗
4. Automatic recognition and classification of cattle chewing activity by an acoustic monitoring method with a single-axis acceleration sensor10.1016/j.compag.2013.01.001 ↗
5. Eating and rumination activity in 10 cows over 10 days10.1016/j.rvsc.2015.05.001 ↗
6. Technical note: Evaluation of a new system for measuring feeding behavior of dairy cows10.1016/j.compag.2014.06.010 ↗
7. Engineering to support wellbeing of dairy animals10.1017/S0022029916000261 ↗
8. Invited review: Sensors to support health management on dairy farms10.3168/jds.2012-6107 ↗
9. Daviesway Farm Monitoring Systems Australia (2021, November 09). MooMonitor+. Daviesway Farm Monitoring Systems Australia. Available online: https://www.daviesway.com.au/dairy-solutions/farm-monitoring-systems/moomonitor-cow-electronic-heat-detection-animal-health-monitoring.
10. A Zigbee-Based Animal Health Monitoring System10.1109/JSEN.2014.2349073 ↗
11. Baldin, M., Breunig, T., Cue, R., de Vries, A., Doornink, M., Drevenak, J., Fourdraine, R., George, R., Goodling, R., and Greenfield, R. (2021). Integrated Decision Support Systems (IDSS) for Dairy Farming: A Discussion on How to Improve Their Sustained Adoption. Animals, 11.10.3390/ani11072025 ↗
12. Big Data in Smart Farming—A review10.1016/j.agsy.2017.01.023 ↗
13. Symposium review: Real-time continuous decision making using big data on dairy farms10.3168/jds.2019-17145 ↗
14. (2021, November 09). Dairy.com Dairy Farm Analytics. Available online: https://www.dairy.com/dairy-farm-analytics/.
15. Cargill (2021, November 09). Dairy Farmers Milk the Power of Data to Improve Feed and Farm Management Practices with Cargill’s Dairy EnteligenTM. Cargill. Available online: https://www.cargill.com/2017/dairy-farmers-milk-the-power-of-data-to-improve-feed-and-farm.
16. Decision support tools for agriculture: Towards effective design and delivery10.1016/j.agsy.2016.09.009 ↗
17. Heat Stress Interaction with Shade and Cooling10.3168/jds.S0022-0302(94)77149-6 ↗
18. Seasonal variations in the composition of Holstein cow’s milk and temperature-humidity index relationship10.1017/S1751731114000032 ↗
19. Effect of season, month and temperature humidity index on the occurrence of clinical mastitis in dairy heifers10.1017/S2040470016000315 ↗
20. Associations Among Body Condition Score, Body Weight, Somatic Cell Count, and Clinical Mastitis in Seasonally Calving Dairy Cattle10.3168/jds.S0022-0302(07)71546-1 ↗
21. Effect of heat stress on rumen temperature of three breeds of cattle10.1007/s00484-017-1442-x ↗
22. Real-time temperature monitoring for the early detection of mastitis in dairy cattle: Methods and case researches10.1016/j.compag.2019.04.004 ↗
23. Behavioral and patho-physiological response as possible signs of pain in dairy cows during Escherichia coli mastitis: A pilot study10.3168/jds.2017-12796 ↗
24. Zhao, S., Min, L., Zheng, N., and Wang, J. (2019). Effect of Heat Stress on Bacterial Composition and Metabolism in the Rumen of Lactating Dairy Cows. Animals, 9.10.3390/ani9110925 ↗
25. A cow-level association of ruminal pH on body condition score, serum beta-hydroxybutyrate and postpartum disorders in Thai dairy cattle10.1111/asj.12217 ↗
26. Animal-based measurements of the severity of mastitis in dairy cows10.1136/vr.163.6.175 ↗
27. When do dry cows get heat stressed? Correlations of rectal temperature, respiration rate, and performance10.3168/jdsc.2019-18019 ↗
28. Becker, C.A., Woolums, A.R., and Stone, A.E. (2021, November 09). Recognizing Heat Stress in Dairy Cattle: How to Visually Record Respiration Rate. Mississippi State University—Extension. Available online: http://extension.msstate.edu/publications/recognizing-heat-stress-dairy-cattle-how-visually-record-respiration-rate.
29. Haan, M.M. (2021, November 09). Using Rumination Sensors to Monitor Heat Stress in Dairy Cows. PennState Extension. Available online: https://extension.psu.edu/using-rumination-sensors-to-monitor-heat-stress-in-dairy-cows.
30. Daily rumination time of lactating dairy cows under heat stress conditions
31. Impact of acute clinical mastitis on cow behaviour10.1016/j.applanim.2011.04.005 ↗
32. Allen, J.D., Anderson, S.D., Collier, R.J., and Smith, J.F. (2013, January 6–8). Managing heat stress and its impact on cow behavior. Proceedings of the 28th Annual Southwest Nutrition and Management Conference, Reno, NV, USA.
33. USDA (2021, November 09). Agricultural Research Service. Recognizing Heat Stress. U.S. Department of Agriculture, Available online: https://www.ars.usda.gov/plains-area/clay-center-ne/marc/docs/heat-stress/recognizing-heat-stress/.
34. South Dakota State University Extension (2021, November 09). Dealing With Heat Stress in Dairy Cows. Available online: https://extension.sdstate.edu/dealing-heat-stress-dairy-cows.
35. Heat Stress and Dairy Cow: Impact on Both Milk Yield and Composition10.3923/ijds.2017.1.11 ↗
36. The Effect of Season on Somatic Cell Count and the Incidence of Clinical Mastitis10.3168/jds.2006-567 ↗
37. Heat stress in lactating dairy cows: A review10.1016/S0301-6226(01)00330-X ↗
38. Composition of Colostrum from Dairy Heifers Exposed to High Air Temperatures During Late Pregnancy and the Early Postpartum Period10.3168/jds.S0022-0302(97)76005-3 ↗
39. Effect of heat stress on milk production in dairy cows10.2298/BAH1103017J ↗
40. Response of Lactating Dairy Cows to Fat Supplementation During Heat Stress10.3168/jds.S0022-0302(91)78435-X ↗
41. The effects of heat stress on protein metabolism in lactating Holstein cows10.3168/jds.2016-11913 ↗
42. Use of physiological parameters to predict milk yield and feed intake in heat-stressed dairy cows10.1016/j.jtherbio.2004.08.051 ↗
43. Bovine subclinical mastitis reduces milk yield and economic return10.1016/j.livsci.2018.01.016 ↗
44. Effect of subclinical mastitis on milk composition
45. Associations of the first occurrence of pathogen-specific clinical mastitis with milk yield and milk composition in dairy cows10.1017/S0022029918000456 ↗
46. Invited review: Low milk somatic cell count and susceptibility to mastitis10.3168/jds.2018-14593 ↗
47. Genetic Analysis of Milk Urea Nitrogen and Lactose and Their Relationships with Other Production Traits in Canadian Holstein Cattle10.3168/jds.2006-487 ↗
48. Quarter Milking for Improved Detection of Increased SCC10.1111/j.1439-0531.2006.00803.x ↗
49. Cell junction disruption after 36 h milk accumulation was associated with changes in mammary secretory tissue activity and dynamics in lactating dairy goats
50. Proteolysis in Milk During Experimental Escherichia coli Mastitis10.3168/jds.S0022-0302(04)73423-2 ↗
51. Board-invited review: Using behavior to predict and identify ill health in animals10.2527/jas.2008-1297 ↗
52. Mainau, E., Temple, D., and Manteca, X. (2021, November 09). Welfare Issues Related to Mastitis. Farm Animal Welfare Education Center. Available online: https://www.fawec.org/en/technical-documents-cattle/130-welfare-issues-related-to-mastitis-in-cows.
53. The Measurement of Observer Agreement for Categorical Data10.2307/2529310 ↗
54. Rossum, G.V. (2011). The Python Language Reference Manual, Network Theory Ltd.
55. Mensching, A., Zschiesche, M., Hummel, J., Schmitt, A.O., Grelet, C., and Sharifi, A.R. (2020). An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data. Animals, 10.10.3390/ani10081412 ↗
56. Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows10.3168/jds.2020-19516 ↗
57. Forecasting house prices in Iran using GMDH10.1108/IJHMA-05-2020-0067 ↗
58. Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter10.1016/j.flowmeasinst.2020.101804 ↗