Version 1
: Received: 20 July 2023 / Approved: 20 July 2023 / Online: 21 July 2023 (11:15:43 CEST)
How to cite:
Kim, J. H.; Colaco, S. J.; Poulose, A.; Neethirajan, S.; Han, D. S. Advancing Pig Welfare Assessment: Introducing the SSPD-PER Method for Objective and Reliable Pig Emotion Recognition. Preprints2023, 2023071510. https://doi.org/10.20944/preprints202307.1510.v1
Kim, J. H.; Colaco, S. J.; Poulose, A.; Neethirajan, S.; Han, D. S. Advancing Pig Welfare Assessment: Introducing the SSPD-PER Method for Objective and Reliable Pig Emotion Recognition. Preprints 2023, 2023071510. https://doi.org/10.20944/preprints202307.1510.v1
Kim, J. H.; Colaco, S. J.; Poulose, A.; Neethirajan, S.; Han, D. S. Advancing Pig Welfare Assessment: Introducing the SSPD-PER Method for Objective and Reliable Pig Emotion Recognition. Preprints2023, 2023071510. https://doi.org/10.20944/preprints202307.1510.v1
APA Style
Kim, J. H., Colaco, S. J., Poulose, A., Neethirajan, S., & Han, D. S. (2023). Advancing Pig Welfare Assessment: Introducing the SSPD-PER Method for Objective and Reliable Pig Emotion Recognition. Preprints. https://doi.org/10.20944/preprints202307.1510.v1
Chicago/Turabian Style
Kim, J. H., Suresh Neethirajan and Dong Seog Han. 2023 "Advancing Pig Welfare Assessment: Introducing the SSPD-PER Method for Objective and Reliable Pig Emotion Recognition" Preprints. https://doi.org/10.20944/preprints202307.1510.v1
Abstract
The utilization of Pig Emotion Recognition (PER) driven by Artificial Intelligence (AI) promises to mitigate labor costs and alleviate stress among domestic pigs, thereby minimizing the need for consistent human intervention. Nevertheless, this research acknowledges the inherent limitations within the raw PER datasets, which often include irrelevant porcine features, hence impeding genuine progress in real-world evaluations. A significant proportion of PER datasets derive from sequential pig imagery obtained from video recordings, and a common pitfall in these studies is the unregulated shuffling of data. This lack of control can result in the overlap of data samples between training and testing groups, thereby yielding skewed experimental evaluations. To address these challenges, this paper introduces a novel solution in the form of the Semi-Shuffle-Pig Detector (SSPD) for PER datasets, with the intent to facilitate a less biased experimental output. By applying the SSPD, we can ensure that all testing data samples remain distinct from the training datasets, and any superfluous information from raw images is systematically discarded. This optimized method enhances the true performance of classification, providing unbiased experimental evaluations. Notably, our approach has led to a remarkable improvement in the Isolation After Feeding (IAF) metric by 20.2\% and achieved higher accuracy in segregating IAF and Paired After Feeding (PAF) classifications exceeding 92\%. This methodology, thereby, ensures the preservation of pertinent data within the PER system and eliminates potential biases in experimental evaluations. Consequently, it elevates the accuracy and reliability of real-world PER applications, resulting in a tangible positive impact on both pig welfare management and food safety standards.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
