Validation of an IoT System Using UHF RFID Technology for Goose Growth Monitoring

• Published in: Agriculture (Basel) Vol. 14; no. 1; p. 76
• Main Authors: Černilová, Barbora, Linda, Miloslav, Kuře, Jiří, Hromasová, Monika, Chotěborský, Rostislav, Krunt, Ondřej
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Accuracy; Animal behavior; Animal husbandry; Animal welfare; Animals; Aquatic birds; Automation; Behavior; Contact stresses; Data acquisition; Data acquisition systems; Data collection; Decision making; Energy consumption; Environmental monitoring; Farming; Geese; Global positioning systems; GPS; Growth; growth monitoring; Humidity; Internet of Things; Livestock; Livestock farms; Localization; Monitoring systems; Poultry; poultry feeding behavior; Radio frequency identification; Radio frequency identification (RFID); Radio-tagging; Regression analysis; Sensitivity analysis; Sensors; Temperature; UHF RFID; Waterfowl; Webcams; Weighing machines; Weight; Weight measurement; Czech Republic
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14010076

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.