Development of an Early Warning System For a Broiler House Using Image Interpretation

Anomalous animal behavior and reduced growth rate are just a few signs that can indicate an undesired situation in a broiler house. It is important that problems such as diseases, technical malfunctioning in feeding and drinking lines and suboptimal management procedures are detected in an early stage to avoid harming the welfare or the production results of broilers. This paper introduces an automated method to detect problems in a broiler house using cameras and an image analysis software. An automated tool for monitoring animal behavior was employed which used camera technology in the broiler house with a dimension of 19.8 by 63.5 meters. Three top view cameras mounted in the ridge of the house at the height of 5 meters continuously monitored the floor space below. Analysis software translated these images into animal distribution index in the house. The final objective was to develop a system that could report malfunctioning in a broiler house to the farmer in real-time. In an experiment with Ross 308 broilers, distribution index data were collected every 5 minutes in a commercial broiler house with 28000 animals. Based on the distribution index data, a linear real-time model was developed and tested to model the animal distribution index as a response to the light input. Using this model, an online prediction could be made on animal distribution index. By comparing the predicted values with the measurements in real-time, malfunctioning could be detected. Results showed that this method was able to report 95.24 per cent (20 out of 21) of events in real-time, demonstrating a high potential of using automatic monitor tools for the monitoring of broiler production over a complete growing period.status: publishe

Diet change rather than diet composition causes transient intestinal damage and growth retardation in pigs

Resistant starch enhances intestinal health and has proven to protect against infections and diseases of the digestive system. It remains to be investigated if the effect of resistant starch can be measured by health indicators such as intestinal fatty acid binding protein (I-FABP), a biomarker for small intestinal health2, and the acute phase protein haptoglobin (Hp), a biomarker related to inflammation and growth.status: publishe

Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities

Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/second) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (Rs=0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a 3 times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level.publisher: Elsevier articletitle: Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities journaltitle: Livestock Science articlelink: http://dx.doi.org/10.1016/j.livsci.2013.12.011 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe

Performance of an Image Analysis Processing System for Hen Tracking in an Environmental Preference Chamber

Image processing systems have been widely used in monitoring livestock for many applications, including identification, tracking, behavior analysis, occupancy rates and activity calculations. The primary goal of this work was to quantify image processing performance when monitoring laying hens by comparing length of stay in each compartment as detected by the image processing system with the actual occurrences registered by human observations. In this work, an image processing system was implemented and evaluated for use in an Environmental Animal Preference Chamber to detect hen navigation between four compartments of the chamber. One camera was installed above each compartment to produce top-view images of the whole compartment. An ellipse-fitting model was applied to captured images to detect whether the hen was present in a compartment. During a choice-test study, mean ± SD success detection rates of 95.9 ± 2.6% were achieved when considering total duration of compartment occupancy. These results suggest that the image processing system is currently suitable for determining the response measures for assessing environmental choices. Moreover, the image processing system offered a comprehensive analysis of occupancy while substantially reducing data processing time compared to the time-intensive alternative of manual video analysis. The above technique was used to monitor ammonia aversion in the chamber. As a preliminary pilot study, different levels of ammonia were applied to different compartments while hens were allowed to navigate between compartments. Using the automated monitor tool to assess occupancy, a negative trend of compartment occupancy with ammonia level was revealed, though further examination is needed.status: publishe

Automatic Monitoring of Pig Activity Using Image Analysis

The purpose of this study is to investigate the feasibility and validity of an automated image processing method to detect the active pigs housed under experimental conditions. Top-view video images were captured for forty piglets, housed ten per pen. On average, piglets had a weight of 27 kg (SD = 4.4 kg) kilograms at the start of experiments and 40kg (SD=6.5) at the end. Each pen was monitored by a top-view CCD camera. The image analysis protocol to automatically quantify activity consisted of several steps. First, in order to localise the pigs, ellipse fitting algorithms were employed. Subsequently, activity was calculated by subtracting image background and comparing binarised images. To validate the results, they were compared to manually labelled behavioural data ('active' versus 'inactive'). This is the first study to show that active pigs in a group can be detected using image analysis with an accuracy of 89.8 %. Since being active is known to be associated with the behavioural status, careful monitoring can give an indication of the health and welfare of pigs.status: publishe

Automatic Monitoring of Pig Locomotion Using Image Analysis

The purpose of this study is to investigate the feasibility and validity of an automated image processing method to detect the locomotion of pigs housed under experimental conditions. Top-view video images were captured for forty piglets, housed ten per pen. On average, piglets had a weight of 27 kg (SD = 4.4 kg) at the start of experiments and 40kg (SD=6.5) at the end. Each pen was monitored by a top-view CCD camera. The image analysis protocol to automatically quantify locomotion consisted localising pigs through background subtraction and tracking them over time. To validate the accuracy of detecting pigs “In Locomotion” or “Not In Locomotion”, they were compared to offline manually labelled behavioural data ('In Locomotion' versus 'Not In Locomotion'). This is the first study to show that locomotion of “pigs in a group” can be determined using image analysis with an accuracy of 89.8 %. Since locomotion is known to be associated with issues such as lameness, careful monitoring can give an accurate indication of the health and welfare of pigs.publisher: Elsevier articletitle: Automatic monitoring of pig locomotion using image analysis journaltitle: Livestock Science articlelink: http://dx.doi.org/10.1016/j.livsci.2013.11.007 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe

Automatic identification of marked pigs in a pen using image pattern recognition

Individual identification in pigs is a key point for management. Many behaviors such as resting, activity, feeding and drinking are better to be monitored individually. The purpose of this work was to investigate feasibility of an automated method to identify marked pigs in a pen in experimental conditions and for behavior-related research by using image processing. First, ellipse fitting algorithms were employed to localize pigs. Second, individual pigs could be identified by their respective paint pattern using pattern recognition techniques. In total, pigs could be identified with an average accuracy of 89.4%. It was also shown that behaviors such as resting can be monitored using the presented technique

Automatic Identification of Marked Pigs in a Pen Using Image Pattern Recognition

The purpose of this work was to investigate feasibility of an automated method to identify marked pigs in a pen in different light conditions by using image processing. This study comprised measurements on four groups of piglets, with 10 piglets per group in a pen. On average, piglets had a weight of 27±4.4 kilograms at the start of experiments and 40kg ± 6.5 at the end. For the purpose of individual identification, basic patterns were painted on the back of the pigs. Each pen was monitored by a top-view CCD camera. Ellipse fitting algorithms were employed to localise pigs. Consequently, individual pigs could be identified by their respective paint pattern using pattern recognition techniques. Taking visual labelling of videos by an experienced ethologist as the gold standard, pigs could be identified with an average accuracy of 89.4%. It was also shown that behaviours such as resting can be monitored using the presented technique.publisher: Elsevier articletitle: Automatic identification of marked pigs in a pen using image pattern recognition journaltitle: Computers and Electronics in Agriculture articlelink: http://dx.doi.org/10.1016/j.compag.2013.01.013 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe