THE USE OF IMAGE LABELLING TO IDENTIFY PIG BEHAVIOURS FOR THE DEVELOPMENT OF A REAL-TIME MONITORING AND CONTROL TOOL

The behaviour of animals is the most informative indicator of the farm status, reflecting animal responses to the change of their welfare, health or surrounding environment. Complex and continuously changing animal responses could be monitored through automated and real time measurements offered by PLF. The most crucial component of an effective PLF system is a precise real-time algorithm able to detect, quantify or even predict the target behaviour, considering that animals are individually different in their responses. During the process of the development of such an algorithm the input of the expertise in animal ethology and biology is indispensable. Understanding of biological mechanisms is a key element in comprehension of the message given by animal behaviour. One of the most important contributions of the specialist with biological background in algorithm development is labelling.This thesis was particularly dedicated to the labelling and its importance in the process of the development of successful PLF system. The objective of this thesis was application of image labelling technique to contribute to the development of an automatic PLF systems to monitor behaviours of pigs

Housing Environment and Farm Animals' Well-Being

This reprint contains articles from the Special Issue of Animals “Housing Environment and Farm Animals' Well-Being”, including original research, review, and communication related to livestock and poultry environmental management, air quality control, emissions mitigation, and assessment of animal health and well-being

Tracking agonistic behaviors in pigs

Master of ScienceDepartment of Animal Sciences and IndustryLindsey E HulbertModern day animal production is intensively increasing to meet global demand for animal products. Producers must balance the increased demand for animal product and instill trust in consumers. Pigs raised in intensive production system display more fighting and unresolved conflict than wildtype pigs. This conflict is called “agonistic interactions”. These undesired behaviors occur mainly at the finishing stage of pigs when resources (water, food, space etc.) becomes limited or when animals meet unfamiliar pen mates. Chronic stress from unresolved conflict is an indication of poor animal welfare and may lead to reduced product quality. The first step in reducing the conflict is finding an efficient system to detect and track pigs at the individual level. Precision animal management is the incorporation of information technology into animal production to monitor animals online, which are supported with artificial intelligence to collect and analyze data that will help to sustainably improve livestock farming. While many systems exist, visual tracking has a great potential for commercial application because it is the least invasive. These systems will, therefore, be useful to producers by providing an early detection of agonistic behaviors in herd, provide timely intervention to compromised animals thereby increasing economic gains

The role of European big game (Capreolus capreolus and Sus scrofa) as hosts for ticks and in the epidemiological life cycle of tick-borne diseases

European wild boar and roe deer belong to the most common, synanthropic and widespread species of big game in Europe. They are potential key hosts for ticks and are hypothesized to play an important role in the life cycle of tick-borne diseases. From August 2011 to February 2014, 247 European roe deer and 344 wild boar were investigated for tick prevalence, abundance, preferred attachment sites and pathogen infections in a forest in southern Germany (the Bienwald, Rhineland-Palatinate). ..

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI