Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Measuring fluctuating asymmetry in fattening rabbits: a valid indicator of performance and housing quality?

Fluctuating asymmetry (FA) has been advocated as the preferred measure of developmental instability and a reliable indicator of the quality of an animal (performance/fitness) and of its environment during its growing life. Empirical studies, however, are too scant or equivocal to consider this assumption adequately validated, which is partly due to the lack of a robust methodological framework for collecting and analyzing FA data. Therefore, we conducted an experiment in which 306 weaned rabbits were housed either in welfare-friendly pens (n = 6) or barren pens (n = 6). The size of both types of pen was similar (1.91 m(2)), but the welfare-friendly pens were equipped with suitable enrichment material (gnawing stick, elevated platform, and hiding box) and were stocked with one-half of the number of rabbits compared with the barren pens (17 vs. 34 rabbits per pen). Performance data (BW gain, ADFI, and G:F) were collected every 14 d. After slaughter (d 63 to 72), we measured twice the left- and right-hand side of 11 presumed bilateral traits on intact carcasses and 50 traits on fleshed bones. Using a stringent decision process, an optimal combination of morphological traits for estimating FA in fattening rabbits was determined. This combination consisted of five traits (fleshed bones) that showed no directional asymmetry or antisymmetry and showed a high level of FA relative to the measurement error; also, these traits were not correlated in their signed FA values. Measurements on intact carcasses seemed inappropriate for estimating FA. Using this robust FA measuring protocol, rabbits housed in the welfare-friendly pens were less asymmetric than were rabbits from the barren pens. Except for a greater daily BW gain in the welfare-friendly pens during the first 14 d after weaning, there were no effects of housing conditions on performance traits. The FA was negatively correlated with BW gain in rabbits from the barren pens, whereas in the welfare-friendly pens, there was no correlation. These results support the application of FA as an indicator of animal welfare and performance; however, FA seems to be a more reliable estimator of the underlying developmental instability when living conditions are suboptimal

The PigWise project: a novel approach in livestock farming through synergistic performances monitoring at individual level

ABSTRACT Optimizing production systems in agriculture and farming environments can nowadays be helped by advancements developed in other domains. In the field of precision livestock farming, solutions enriched with ICT, robotics and automation components are increasingly used to improve processes' efficiency and flexibility. This paper proposes a pervasive ICT system to monitor and record eating behavior of fattening pigs, leveraging on HF RFID ear tags identifiers (to detect animal eating while the head is on the trough) and on Camera Vision Systems (to cross-validate RFID reading). In addition a Synergistic Control algorithm is applied due to analyze information, extract feeding behaviors and detect eventual issues. Finally, these information are made available on the network, to the end-user, through the Virtus Middleware: it is an Internet of Things C0100 Scalera A., Brizzi P., Tomasi R., Gregersen T., Mertens K., Maselyne J. ,Van Nuffel A., Hessel E., Van den Weghe H. "The PigWise project: a novel approach in livestock farming through synergistic performances monitoring at individual level". EFITA-WCCA-CIGR Conference "Sustainable Agriculture through ICT Innovation", Turin, Italy, 24-27 June 2013. (IoT) system enabling seamless data integration and event sharing, able to manage heterogeneous information sources and geographically-distributed, large-scale deployments