Validation of an IoT System Using UHF RFID Technology for Goose Growth Monitoring
-
Published:2023-12-30
Issue:1
Volume:14
Page:76
-
ISSN:2077-0472
-
Container-title:Agriculture
-
language:en
-
Short-container-title:Agriculture
Author:
Černilová Barbora1ORCID, Linda Miloslav1, Kuře Jiří1ORCID, Hromasová Monika1ORCID, Chotěborský Rostislav2, Krunt Ondřej3
Affiliation:
1. Department of Electrical Engineering and Automation, Faculty of Engineering, Czech University of Life Sciences Prague, Kamycka 129, 165 00 Prague, Czech Republic 2. Department of Material Science and Manufacturing Technology, Faculty of Engineering, Czech University of Life Sciences Prague, Kamycka 129, 165 00 Prague, Czech Republic 3. Department of Animal Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
Abstract
Regular weight measurement is important in fattening geese to assess their health status. Failure to gain weight may indicate a potential illness. Standard weight gain analysis involves direct contact with the animal, which can cause stress to the animal, resulting in overall negative impacts on animal welfare. The focus of this study was to design a smart solution for monitoring weight changes in the breeding of farm animals. The proposed IoT system with a weighing device equipped with RFID technology for animal registration aimed to minimize the negative aspects associated with measuring in contact with humans. The proposed system aims to incorporate modern approaches in animal husbandry and use these obtained data for the potential development of husbandry approaches for different breeds of animals and enhanced managerial decision-making within husbandry. The system consisted of three main components: a data acquisition system, a weighing system with RFID, and an environmental monitoring system. In this study, the RFID system accuracy for detecting geese in the weighing system environment was assessed. The entire system evaluation yielded a sensitivity of 95.13%, specificity of 99.89%, accuracy of 99.78%, and precision of 95.01%. Regression analysis revealed a good correlation between observed feeding and RFID registrations with a determination coefficient of R2 = 0.9813.
Funder
Faculty of Engineering, Czech University of Life Sciences Prague
Subject
Plant Science,Agronomy and Crop Science,Food Science
Reference31 articles.
1. Bortoň, L., and Štolcová, M. (2019). Tools of Precision Agriculture in Dairy Cattle Farms, Česká Technologická Platforma Pro Zemědělství. Available online: https://www.ctpz.cz/vyzkum/nastroje-precizniho-zemedelstvi-v-chovech-dojeneho-skotu-910. 2. Zhang, Y., Ge, Y., Yang, T., Guo, Y., Yang, J., Han, J., Gong, D., and Miao, H. (2022). An IoT-Based Breeding Egg Identification and Coding System for Selection of High-Quality Breeding Geese. Animals, 12. 3. Vázquez Diosdado, J.A., Barker, Z.E., Hodges, H.R., Amory, J.R., Croft, D.P., Bell, N.J., and Codling, E.A. (2015). Classification of Behaviour in Housed Dairy Cows Using an Accelerometer-Based Activity Monitoring System. Anim. Biotelemetry, 3. 4. Brahim, A., Malika, B., Rachida, A., Mustapha, L., Mehammed, D., and Mourad, L. (2020, January 2–3). Dairy Cows Real Time Behavior Monitoring by Energy-Efficient Embedded Sensor. Proceedings of the 2020 2nd International Conference on Embedded and Distributed Systems, EDiS 2020, Oran, Algeria. 5. Methods to Construct Feeding Visits from RFID Registrations of Growing-Finishing Pigs at the Feed Trough;Maselyne;Comput. Electron. Agric.,2016
|
|