# Emotion Recognition in Horses with Convolutional Neural Networks
## 4 Discussion  
This paper describes how an AI-based system capable of detecting emotions of animals was created and able to assess behaviors indicating emotions in horses. The system does so in an autonomous way and with good results. This proves that we are capable of creating a system that can recognize the emotions of a non-human animal species that has the ability to produce facial expressions and that it might be possible to detect these emotions by other methods, such as measuring the animal's heart rate, its temperature, or recording the sounds that they produce and feeding all this data into a system similar to the one created here [41]. While the system works with reasonable accuracy, it is worth pointing out that it could be improved in many ways.  
First of all, we have to keep in mind that predicting emotions is a complex task, and it is hard for humans and it is even harder for animals.  
Figure 11: Graphical user interface.  
Figure 10: GradCAM overlays of the chosen test images.  
Secondly, there were only 440 labeled images in total, which is not a large number for systems like this one. Obtaining the 440 horse emotion pictures was the most time-consuming part of our research, as these pictures were not publicly available and had to be manually taken and labeled by the authors. Future research obtaining more labeled horse emotion pictures will be essential, and we hope to initiate a citizen science initiative towards that goal. Having more images, thousands or millions of them split evenly between every emotion will make future emotion prediction models more accurate and generalizable.  
Thirdly, only the head and neck of the horse were used to predict its emotion, and analyzing cues from the entire body would most likely yield better results; however, since we would have more features to analyze, more data would be needed. In addition, if the entire body is used, the emotions have to be reviewed, since they were defined only by cues found in the head and neck of the horse.  
Fourthly, ear, neck, and body positions indicative of any general emotion at a distance may also indicate more severe problems upon closer examination. Many of the studies on the equine pain face, for example, show ear and neck positions similar to those of relaxed horses. In order to use such a tool to improve our understanding of equine emotions and promote better equine welfare, this generalized emotional assessment tool could be combined with the existing research and machine learning in equine facial expressions to eventually create a more robust program that takes into account not only the generalized emotional expression, but also the nuances of pain or distress that could be mistaken for something else. This lack of differentiation between subtle emotion patterns is also evident in the confusion matrix in Figure 9, which reveals that emotions are often confused with the \"curious\" and \"relaxed\" states of a horse.  
Finally, another way to improve the results obtained in this paper would be to use information from other sources in combination with the images. Sensors to measure heart rate and temperature could be put on horses, their sound could be recorded, etc.  
With additional adjustments, this tool could serve as an important means of supporting the need to look at animal behavior and emotions as a way to improve animal welfare in various agricultural industries including in clinical veterinary settings [9; 10; 42]. There is a growing push to create better assessment tools that look at improving equine psychological welfare in domestic states through more robust measurements of emotional affect via behavioral observations [21; 43] and an automated system that relies on research-based behavioral assessments rather than objective interpretations could help improve the accuracy of assessment of the psychological welfare of domestic livestock.