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

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5

Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis

<p/> <p>Accurate iris detection is a crucial part of an iris recognition system. One of the main issues in iris segmentation is coping with occlusion that happens due to eyelids and eyelashes. In the literature, some various methods have been suggested to solve the occlusion problem. In this paper, two different segmentations of iris are presented. In the first algorithm, a circle is located around the pupil with an appropriate diameter. The iris area encircled by the circular boundary is used for recognition purposes then. In the second method, again a circle is located around the pupil with a larger diameter. This time, however, only the lower part of the encircled iris area is utilized for individual recognition. Wavelet-based texture features are used in the process. Hamming and harmonic mean distance classifiers are exploited as a mixed classifier in suggested algorithm. It is observed that relying on a smaller but more reliable part of the iris, though reducing the net amount of information, improves the overall performance. Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31%. The sensitivity of the proposed method is analyzed versus contrast, illumination, and noise as well, where lower sensitivity to all factors is observed when the lower half of the iris is used for recognition.</p

Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis

Accurate iris detection is a crucial part of an iris recognition system. One of the main issues in iris segmentation is coping with occlusion that happens due to eyelids and eyelashes. In the literature, some various methods have been suggested to solve the occlusion problem. In this paper, two different segmentations of iris are presented. In the first algorithm, a circle is located around the pupil with an appropriate diameter. The iris area encircled by the circular boundary is used for recognition purposes then. In the second method, again a circle is located around the pupil with a larger diameter. This time, however, only the lower part of the encircled iris area is utilized for individual recognition. Wavelet-based texture features are used in the process. Hamming and harmonic mean distance classifiers are exploited as a mixed classifier in suggested algorithm. It is observed that relying on a smaller but more reliable part of the iris, though reducing the net amount of information, improves the overall performance. Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31&#37;. The sensitivity of the proposed method is analyzed versus contrast, illumination, and noise as well, where lower sensitivity to all factors is observed when the lower half of the iris is used for recognition