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

Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows

The objective of this study was to determine if a 3-dimensional computer vision automatic locomotion scoring (3D-ALS) method was able to outperform human observers for classifying cows as lame or nonlame and for detecting cows affected and nonaffected by specific type(s) of hoof lesion. Data collection was carried out in 2 experimental sessions (5 months apart)

Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle

Currently, diagnosis of lameness at an early stage in dairy cows relies on visual observation by the farmer, which is time consuming and often omitted. Many studies have tried to develop automatic cow lameness detection systems. However, those studies apply thresholds to the whole population to detect whether or not an individual cow is lame. Therefore, the objective of this study was to develop and test an individualized version of the body movement pattern score, which uses back posture to classify lameness into 3 classes, and to compare both the population and the individual approach under farm conditions. In a data set of 223 videos from 90 cows, 76% of cows were correctly classified, with an 83% true positive rate and 22% false positive rate when using the population approach. A new data set, containing 105 videos of 8 cows that had moved through all 3 lameness classes, was used for an ANOVA on the 3 different classes, showing that body movement pattern scores differed significantly among cows. Moreover, the classification accuracy and the true positive rate increased by 10 percentage units up to 91%, and the false positive rate decreased by 4 percentage units down to 6% when based on an individual threshold compared with a population threshold

Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows

In this study, two different computer vision techniques to automatically measure the back posture in dairy cows were tested and evaluated. A two-dimensional and a three-dimensional camera system were used to extract the back posture from walking cows, which is one measurement used by experts to discriminate between lame and not lame cows. So far, two-dimensional cameras positioned in side view are used to measure back posture. This method, however, is not always applicable in farm conditions since it can be difficult to be installed. Shadows and continuous changes in the background also render image segmentation difficult and often erroneous. In order to overcome these problems, a new method to extract the back posture by using a three-dimensional camera from top view perspective is presented in this paper. The experiment was conducted in a commercial Israeli dairy farm and a dataset of 273 cows was recorded by both the three-dimensional and two-dimensional cameras. The classifications of both the two-dimensional and the three-dimensional algorithms were evaluated against the visual locomotion scores given by an expert veterinary. The two-dimensional algorithm had an accuracy of 91%, while the three-dimensional algorithm had an accuracy of 90% on the evaluation dataset. These results show that the application of a three-dimensional camera leads to an accuracy comparable to the side view approach and that the top view approach can overcome limitations in terms of automation and processing time

4.6. Discussion: PLF in genetics & health of beef, calves and heifers

This chapter is the result of the joint session hosted by the EAAP (European Federation of Animal Science) and EU-PLF (Precision Livestock Farming) held in Copenhagen 2014. Following the full-length peer-reviewed papers, presented in Chapters 4.1 – 4.5, this chapter brings together ‘questions and answers' debates during this session’s discussions. Unique of this ‘cross-disciplinary’ approach is that animal nutritionists, animal geneticists, animal behaviourists, health and welfare scientists, zoologists, and biologists (i.e. animal-focused scientists), that usually participate in the EAAP meetings, as well as industries, farmers, and PLF engineers raised up further research ideas, contradicting opinions as well as unsolved issues. They concluded that (1) PLF is a management tool that allows farmers to make better decisions based on animal data; (2) it has the potential to support animal feed suppliers, human-food retailers, environment carers, policy makers, and other players along the livestock chain; and (3) that the current challenge for PLF is the integration of the sensors in the majority of the farms and not only to the pioneering farms. This discussion chapter would be interesting to those who are interested in further research (the recorded discussions), companies (ready-to-market applications are described in the papers), extension service and knowledge transfer units, farmers, farm animal protectors activists (sensors for caring farm animals), and all those who ‘touch’ the PLF various fields

3.5. Discussion: how PLF delivers added value to farmers

This chapter is the result of the joint session hosted by the EAAP (European Federation of Animal Science) and EU-PLF (Precision Livestock Farming) held in Copenhagen 2014. Following the full-length peer-reviewed papers, presented in Chapters 3.1 – 3.4, this chapter brings together ‘questions and answers' debates during this session’s discussions. Unique of this ‘cross-disciplinary’ approach is that animal nutritionists, animal geneticists, animal behaviourists, health and welfare scientists, zoologists, and biologists (i.e. animal-focused scientists), that usually participate in the EAAP meetings, as well as industries, farmers, and PLF engineers raised up further research ideas, contradicting opinions as well as unsolved issues. They concluded that (1) PLF is a management tool that allows farmers to make better decisions based on animal data; (2) it has the potential to support animal feed suppliers, human-food retailers, environment carers, policy makers, and other players along the livestock chain; and (3) that the current challenge for PLF is the integration of the sensors in the majority of the farms and not only to the pioneering farms. This discussion chapter would be interesting to those who are interested in further research (the recorded discussions), companies (ready-to-market applications are described in the papers), extension service and knowledge transfer units, farmers, farm animal protectors activists (sensors for caring farm animals), and all those who ‘touch’ the PLF various fields

8.5. Discussion: rumen sensing, feed intake & precise feeding

This chapter is the result of the joint session hosted by the EAAP (European Federation of Animal Science) and EU-PLF (Precision Livestock Farming) held in Copenhagen 2014. Following the full-length peer-reviewed papers, presented in Chapters 8.1 – 8.4, this chapter brings together ‘questions and answers' debates during this session’s discussions. Unique of this ‘cross-disciplinary’ approach is that animal nutritionists, animal geneticists, animal behaviourists, health and welfare scientists, zoologists, and biologists (i.e. animal-focused scientists), that usually participate in the EAAP meetings, as well as industries, farmers, and PLF engineers raised up further research ideas, contradicting opinions as well as unsolved issues. They concluded that (1) PLF is a management tool that allows farmers to make better decisions based on animal data; (2) it has the potential to support animal feed suppliers, human-food retailers, environment carers, policy makers, and other players along the livestock chain; and (3) that the current challenge for PLF is the integration of the sensors in the majority of the farms and not only to the pioneering farms. This discussion chapter would be interesting to those who are interested in further research (the recorded discussions), companies (ready-to-market applications are described in the papers), extension service and knowledge transfer units, farmers, farm animal protectors activists (sensors for caring farm animals), and all those who ‘touch’ the PLF various fields

2.5. Discussion: PLF applications of automatic lameness detection

This chapter is the result of the joint session hosted by the EAAP (European Federation of Animal Science) and EU-PLF (Precision Livestock Farming) held in Copenhagen 2014. Following the full-length peer-reviewed papers, presented in Chapters 2.1 – 2.4, this chapter brings together ‘questions and answers' debates during this session’s discussions. Unique of this ‘cross-disciplinary’ approach is that animal nutritionists, animal geneticists, animal behaviourists, health and welfare scientists, zoologists, and biologists (i.e. animal-focused scientists), that usually participate in the EAAP meetings, as well as industries, farmers, and PLF engineers raised up further research ideas, contradicting opinions as well as unsolved issues. They concluded that (1) PLF is a management tool that allows farmers to make better decisions based on animal data; (2) it has the potential to support animal feed suppliers, human-food retailers, environment carers, policy makers, and other players along the livestock chain; and (3) that the current challenge for PLF is the integration of the sensors in the majority of the farms and not only to the pioneering farms. This discussion chapter would be interesting to those who are interested in further research (the recorded discussions), companies (ready-to-market applications are described in the papers), extension service and knowledge transfer units, farmers, farm animal protectors activists (sensors for caring farm animals), and all those who ‘touch’ the PLF various fields