Figure 1 - Measuring emotions in farm animals – sensor approaches. MEASURING EMOTIONS IN FARM ANIMALS – SENSOR APPROACHES Neutral emotional state Biochemical factors Oxytocin
E-MOTION =
Positive emotional state Visible Indicators Ear Postures, Tail Positions, Eye White Region, Orbital tightening, nose or cheek bulge
ENERGY IN MOTION
Negative emotional state
Cortisol Dopamine Corticosterone MiRNA
Sensor measures
EEG-Brain Waves Respiration Rate Blood Volume Pulse Acoustics and Noise
Heart Rate Thermal / Heat Smell / Olfactory
Facial recognition platform
Wearable sensors
The question remains how to match such facial expressions with the correct emotion. To discern an animal’s emotional state, a tremendous amount of gathered data has to be ana- lysed and the correct computer algorithm used to develop proper software. The approach with humans is to utilise machine learning that essentially has a computer react similarly to how a human brain would under certain conditions. A major barrier for this development has been the area of fuzzy logic that looks at responses beyond a computer’s usual yes and no answers or, in other words, the need to create a computerised neural network system to resemble human reactions beyond just the facts. As an example, how one person reacts to a stubbed toe is dif- ferent from another person, which a computer would have trouble differentiating. At some level, the same barrier exists for an animal algorithm that must be further delineated by species and environment. Do wild animals have different emotional responses from farm animals? To have accurate facial recognition is mandatory as, like hu- mans, animals have their individual levels of stress and must be evaluated separately and by species. So far, results of ani- mals are from controlled environments, such as a farm, with- out a comparative measurement or benchmark of free range or wild animals. Some kind of baseline must be used to meas- ure animal emotions by species in their natural environment, but such data are currently unavailable due to a lack of any suitable monitoring equipment.
Managing facial recognition data Business economics has produced mega-farms containing large numbers of animals, making the task of identifying each animal arduous, not to mention expensive when using multi- ple sensors. A single sensor capable of collecting essential data would be a milestone by being more affordable and would help increase the health and productivity of livestock, while reducing stress levels by being as simple as having a picture taken. The use of edge computing, or having dedicat- ed data interpreted independently rather than through a data centre, needs exploring so sensor devices themselves can produce findings and reduce the amount of bandwidth. That would be especially helpful on farms with a poor inter- net connection. Computer programmes, like WUR Wolf, de- veloped by the Farmworx group in the Netherlands’ Wagenin- gen University & Research, analysed animal facial features. The programme recognises and evaluates 14 facial features combinations and seven emotional states from cows and pigs. For the study, images and videos of several thousand pigs and dairy cows were evaluated using You Only Look Once (YOLO) real time object detection. The corresponding data was interpreted by Pycharm and Python computer pro- gramming languages. The deep learning model WUR Wolf was dedicated in identifying facial expressions of these farm animals, successfully identifying 86% of the animals and their emotional states. A spinoff industry from this could be for the many security applications on the farm and elsewhere. Importantly, such detection is being performed humanely
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