Peer-reviewed articles 17,970 +


Title: ANALYSIS OF DAIRY COW BEHAVIORAL PATTERNS USING A COMBINATION OF IOT DATA AND SIGNAL PROCESSING TECHNIQUES
ANALYSIS OF DAIRY COW BEHAVIORAL PATTERNS USING A COMBINATION OF IOT DATA AND SIGNAL PROCESSING TECHNIQUES

PlumX Article Metrics by Elsevier
Kristina Dineva; Tatiana Atanasova
10.5593/sgem2023/6.1/s25.14
10.5593/sgem2023/6.1
1314-2704
978-619-7603-61-3
English
23
6.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Advances in Biotechnology
This article presents a study focused on animal activity recognition using a combination of IoT devices and signal processing techniques. The study involves collecting data from IoT devices placed on the cow's neck, which are equipped with an accelerometer and gyroscope, along with a synchronized video camera. The objective is to accurately recognize and classify four key activities exhibited by the cow.
To prepare the collected signals for analysis, various signal processing techniques are applied. This includes essential pre-processing steps to clean the data, such as noise removal and filtering, ensuring reliable and accurate activity recognition. Additionally, feature extraction processes are performed to enhance the accuracy and precision of behavioral models. The study also examines the boundaries and allowable variations for each specific cow movement. Furthermore, dimensionality reduction techniques are applied to reduce the complexity of the data.
This study aims to develop an approach to analyze the behavior patterns of cows using IoT devices. The results contribute not only to our understanding of cow behavior but also hold potential implications for livestock management, health monitoring, and precision agriculture. This research paves the way for further exploration and development in the field of animal behavior studies, ultimately leading to improved welfare and productivity in livestock management practices.
[1] Cabezas J., Yubero R., Visitacion B., Navarro-Garcia J., Algar M.J., Cano E.L., Ortega F. Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection. Entropy, 2022 doi: 10.3390/e24030336.
[2] Versluijs E., Niccolai L. J., Spedener M., Zimmermann B., Hessle A., Tofastrud M., Devineau O. and Evans A. L. Classification of behaviors of free-ranging cattle using accelerometry signatures collected by virtual fence collars, vol. 4, 2023 https://doi.org/10.3389/fanim.2023.1083272
[3] Li Y, Shu H, Bindelle J, Xu B, Zhang W, Jin Z, Guo L, Wang W. Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods. Animals. 2022; 12(9):1060. https://doi.org/10.3390/ani12091060
[4] Vazquez Diosdado, J.A., Barker, Z.E., Hodges, H.R. et al. Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system. Anim Biotelemetry 3, 15, 2015. https://doi.org/10.1186/s40317-015-0045-8
[5] Godsk, T., Kj?rgaard, M.B. High Classification Rates for Continuous Cow Activity Recognition Using Low-Cost GPS Positioning Sensors and Standard Machine Learning Techniques. ICDM, 2011. https://doi.org/10.1007/978-3-642-23184-1_14
[6] Balasso P., Marchesini G., Ughelini N., Serva L., Andrighetto I. Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal’s Left Flank. Animals. 2021; 11(10):2972. https://doi.org/10.3390/ani11102972
[7] Simanungkalit G., Barwick J., Cowley F., Dobos R., Hegarty R. A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement. Animals. 2021; 11(4):1153. https://doi.org/10.3390/ani11041153
[8] Dittrich, I.; Gertz, M.; Maassen-Francke, B.; Krudewig, K.-H.; Junge, W.; Krieter J. Estimating risk probabilities for sickness from behavioural patterns to identify health challenges in dairy cows with multivariate cumulative sum control charts. Animal 2022, 16(8), 100601, https://doi.org/10.1016/j.animal.2022.100601
[9] Helwatkar, A., Riordan, D. and Walsh, J. Sensor Technology For Animal Health Monitoring, International Journal on Smart Sensing and Intelligent Systems, vol.7, no.5, 2014, pp.1-6. https://doi.org/10.21307/ijssis-2019-057
[10] Bloch V., Frondelius L., Arcidiacono C., Mancino M., Pastell M. Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data. Sensors. 2023; 23(5):2611. https://doi.org/10.3390/s23052611
[11] Dineva, K., Atanasova, T. Machine Learning Solution for IoT Big Data. 20th Int. Multidisciplinary Scientific Geoconference SGEM 2020, 18-24 Albena, Bulgaria, 2.1, 2020, 207-214, https://doi.org/10.5593/sgem2020/2.1/s07.027
The research leading to these results received funding from the Ministry of Education and Science of the Republic Bulgaria under the National Science Program INTELLIGENT ANIMAL HUSBANDRY, grant agreement No. Д01-62/18.03.2021/.
conference
https://doi.org/10.5593/sgem2023/6.1/s25.14
Download PDF
Proceedings of 23rd International Multidisciplinary Scientific GeoConference SGEM 2023
23rd International Multidisciplinary Scientific GeoConference SGEM 2023, 03 - 09 July, 2023
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
121-128
03 - 09 July, 2023
website
9226
IoT, Animal Behavior, Accelerometer, Gyroscope, Pattern Recognition