Developing and applying precision animal farming tools for poultry behavior monitoring

Appropriate measurement of broiler behaviors is critical to optimize broiler production efficiency and improve precision management strategies. However, performance of different precision tools on measuring broiler behaviors of interest remains unclear. This dissertation systematically developed and evaluated radio frequency identification (RFID) system, image processing, and deep learning for automatically detecting and analyzing broiler behaviors. Then different behaviors (i.e., feeding, drinking, stretching, restricted feeding) of broilers under representative management practices were measured using the developed precision tools. The broilers were Ross 708 in weeks 4-8. The major findings show that the RFID system achieved high performance (over 90% accuracy) for continuously tracking feeding and drinking behaviors of individual broilers, after they were customized and modified, such as tag sensitivity test, power adjustment, radio wave shielding, and assessment of interference by add-ons. The image processing algorithms combined with a machine learning model were customized and adjusted based on the experimental conditions and finally achieved 85% sensitivity, specificity, and accuracy for detecting bird number at feeder and at drinkers. After adjusting labeling method and hyperparameter tuning, the faster region-based convolutional neural network (faster R-CNN) had over 86% precision, recall, specificity, and accuracy for detecting broiler stretching behaviors. In comprehensive algorithms, the faster R-CNN showed over 92% precision, recall, and F1 score for detecting feeder, eating birds, and birds around feeder. The bird trackers had a 3.2% error rate to track individual birds around feeder. The support vector machine behavior classifier achieved over 92% performance for classifying walking birds. Image processing model was also developed to detect birds that were restricted to feeder access. Broilers had different behavior responses to different sessions of a day, bird ages, environments, diets, and allocated resources. Reducing stocking density, increasing feeder space, and applying poultry-specific light spectrum and intensity were beneficial for birds to perform behaviors, such as feeding, drinking, and stretching, while using the antibiotics-free diet reduced bird feeding time. In conclusion, the developed tools are useful tools for automated broiler behavior monitoring and the measured behavior responses provide insights into precision management of welfare-oriented broiler production

Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms

: Management of poultry farms in China mostly relies on manual labor. Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents, making it very difficult for data retrieve, processing and analysis. An integrated cloud-based data management system (CDMS) was proposed in this study, in which the asynchronous data transmission, distributed file system, and wireless network technology were used for information collection, management and sharing in large-scale egg production. The cloud-based platform can provide information technology infrastructures for different farms. The CDMS can also allocate the computing resources and storage space based on demand. A real-time data acquisition software was developed, which allowed farm management staff to submit reports through website or smartphone, enabled digitization of production data. The use of asynchronous transfer in the system can avoid potential data loss during the transmission between farms and the remote cloud data center. All the valid historical data of poultry farms can be stored to the remote cloud data center, and then eliminates the need for large server clusters on the farms. Users with proper identification can access the online data portal of the system through a browser or an APP from anywhere worldwide

Animal Welfare Assessment

This Special Issue provides a collection of recent research and reviews that investigate many areas of welfare assessment, such as novel approaches and technologies used to evaluate the welfare of farmed, captive, or wild animals. Research in this Special Issue includes welfare assessment related to pilot whales, finishing pigs, commercial turkey flocks, and dairy goats; the use of sensors or wearable technologies, such as heart rate monitors to assess sleep in dairy cows, ear tag sensors, and machine learning to assess commercial pig behaviour; non-invasive measures, such as video monitoring of behaviour, computer vision to analyse video footage of red foxes, remote camera traps of free-roaming wild horses, infrared thermography of effort and sport recovery in sport horses; telomere length and regulatory genes as novel biomarkers of stress in broiler chickens; the effect of environment on growth physiology and behaviour of laboratory rare minnows and housing system on anxiety, stress, fear, and immune function of laying hens; and discussions of natural behaviour in farm animal welfare and maintaining health, welfare, and productivity of commercial pig herds

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

Exploring the relationship between spatial cognitive ability and movement ecology

Spatial cognitive ability is hypothesised to be a key determinant of animal movement patterns. However, empirical demonstrations linking intra-individual variations in spatial cognitive ability with movement ecology are rare. I reared ~200 simultaneously hatched pheasant chicks per year over three years in standardised conditions without parents, controlling for the confounding effects of experience, maternal influences and age. I tested the chicks on spatial cognitive tasks from three weeks old to obtain measures of inherent, early-life spatial cognitive ability. Each year, I released birds when 10 weeks old into an open-topped enclosure in woodland. Birds dispersed from this enclosure after about one-month. Importantly, all birds were released into the same, novel area simultaneously, thus their experiences and opportunities were standardised. I remotely tracked pheasant movement through either RFID antenna placed under 43 supplementary feeders situated throughout our field site (2016) or by using a novel reverse-GPS tracking system (2017-2018). Spatial cognitive ability, determined through binary spatial discrimination (2016) or a Barnes maze (2017), was related to the diversity of foraging sites an individual used (Chapter 2: 2016). Those with better spatial cognitive ability used a more diverse range of artificial feeders than poor performing counterparts, perhaps to retain a buffer of alternative foraging sites where resource profitability was known. I found no relationship between the timing of daily foraging onset between birds of differing cognitive ability (Chapter 3; 2016), which I had hypothesised to be a consequence of birds developing efficient routes between refuges and feeders. After establishing a reverse GPS system on our field site (Chapter 4: 2017), I collected more detailed information about pheasant movement and found that birds with higher accuracy scores on the cognition tasks initially moved between foraging and resting sites more slowly than inaccurate birds in novel environments, perhaps to gather more detailed information. Accurate birds increased their speed over one month to match the same speed as inaccurate birds. All birds increased the straightness of their routes at a similar rate. Lastly, I found intraspecific differences in the orientation strategy that birds used to solve a dual strategy maze task (Chapter 5: 2018). These differences predicted habitat use after release: birds that utilised landmarks (allocentric strategies) showed less aversion to urban habitats (farm buildings/yards) than egocentric/mixed strategy birds, which is potentially due to the presence of large, stable landmarks within these habitats. In this thesis, I provide several empirical links between spatial cognitive ability and movement ecology across a range of ecological contexts. I suggest that very specific cognitive processes may govern particular movement behaviours and that there is not one overarching general spatial ability.European Commissio