Detection of embryo mortality and hatch using thermal differences among incubated chicken eggs

Accurate diagnosis of both the stage of embryonic mortality and the hatch process in incubated eggs is a fundamental component in troubleshooting and hatchery management. However, traditional methods disturb incubation, destroy egg samples, risk contamination, are time and labour-intensive and require specialist knowledge and training. Therefore, a new method to accurately detect embryonic mortality and hatching time would be of significant interest for the poultry industry if it could be done quickly, cheaply and be fully integrated into the process. In this study we have continuously measured individual eggshell temperatures and the corresponding micro-environmental air temperatures throughout the 21 days of incubation using standard low-cost temperature sensors. Moreover, we have quantified the thermal interaction between eggs and air by calculating thermal profile changes (temperature drop time, drop length and drop magnitude) that allowed us to detect four categories of egg status (infertile/early death, middle death, late death and hatch) during incubation. A decision tree induction classification model accurately (93.3%) predicted the status of 105 sampled eggs in comparison to the classical hatch residue breakout analyses. With this study we have provided a major contribution to the optimisation of incubation processes by introducing an alternative method for the currently practiced hatch residue breakout analyses.status: publishe

Farm animals monitoring tool based on image processing technique

This paper describes camera based systems and image processing technique as a monitoring tool for farm animals. Image based systems were used to automatic measure the activity and occupation indexes of piglets and broilers chickens. Experimental results were presented in a form of two case studies: 1) Understanding the effect of environmental enrichment in piglet's activities - the study was conducted in two selected pens of a fattening room. The activity of 14 Dalland piglets was recorded continuously for a total of 5 days. On the second day environmental enrichments were introduced in the form of two wooden logs and a chain. 2) Understanding the effect of light intensity on broiler chicken's activities - in a total of 62 Ross 308 broiler chickens (equal number of female and male) kept on a 16 h photoperiod treatments. The light intensity schedule varied according to the age of the chickens. For chickens with 15 days old the light-dark schedule alternate every 4 hours between 5 lx and 100 lx. For chickens with around 21 and 40 days old the light conditions alternated every 2 hours. In both study cases the activity index recorded the total amount of movement at a group level. Piglets increased their movements and playing behaviour when environmental enrichments (wood logs and chain) were introduced to the pens. Broiler chickens showed higher activity indexes during periods of 100 lx than 5 lx when light intensity alternated

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

2.2. Risk factors for system performance of an automatic 3D vision locomotion monitor for cows

The aim of this study was to identify the factors that affect the system performance of a threedimensional based vision system for automatic monitoring of dairy cow locomotion implemented on a commercial dairy farm. Data were gathered from a Belgian commercial dairy farm with a 40-stand rotary milking parlour. This resulted in forced cow traffic twice a day when all Holstein cows passed through an alley on their return to the pen. The video recording system with a 3D depth camera, positioned in top-down perspective, was installed in this alley. The entire monitoring process, including video recording, filtering and analysis and cow identification, was automated. System performance was defined as the number of analysed videos per session. To investigate how many video recordings could be used for monitoring dairy cow locomotion, videos were captured during 566 consecutive milking sessions. For each session, 224±10 cows were identified on average by the RFID-antenna, and 197±17 videos were recorded (88.0±6.2%) by the camera. After linking the cow identification to the recorded videos, 178±14 cow videos (79.5±5.7%) were available for analysis. After all video processing, an average of 110±24 recorded cow videos (49.3±11.0%) per session was used for analysis. The number of analysed videos per cow per week was individually variable. Cow traffic in the alley where the recordings were made had a big influence on the performance of the system. Heavy cow traffic reduced the number of recordings and the number of identified cows in each video, and more videos were filtered out due to incorrect cow segmentation in the videos

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm’s daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow’s performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY = 0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4 d before diagnosis; the slope coefficient of the daily milk yield 4 d before diagnosis; the nighttime to daytime neck activity ratio 6 d before diagnosis; the milk yield week difference ratio 4 d before diagnosis; the milk yield week difference 4 d before diagnosis; the neck activity level during the daytime 7 d before diagnosis; the ruminating time during nighttime 6 d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well