Enhancing Animal Welfare with Interaction Recognition: A Deep Dive into Pig Interaction Using Xception Architecture and SSPD-PIR Method

Jung Hwan Kim, Alwin Poulose, Savina Jassica Colaco, Suresh Neethirajan and Dong Seog Han ()
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Jung Hwan Kim: School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro Buk-gu, Daegu 41566, Republic of Korea
Alwin Poulose: School of Data Science, Indian Institute of Science Education and Research, Thiruvananthapuram 695551, India
Savina Jassica Colaco: School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro Buk-gu, Daegu 41566, Republic of Korea
Suresh Neethirajan: Department of Animal Science and Aquaculture, Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada
Dong Seog Han: School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro Buk-gu, Daegu 41566, Republic of Korea

Agriculture, 2023, vol. 13, issue 8, 1-18

Abstract: The advent of artificial intelligence (AI) in animal husbandry, particularly in pig interaction recognition (PIR), offers a transformative approach to enhancing animal welfare, promoting sustainability, and bolstering climate resilience. This innovative methodology not only mitigates labor costs but also significantly reduces stress levels among domestic pigs, thereby diminishing the necessity for constant human intervention. However, the raw PIR datasets often encompass irrelevant porcine features, which pose a challenge for the accurate interpretation and application of these datasets in real-world scenarios. The majority of these datasets are derived from sequential pig imagery captured from video recordings, and an unregulated shuffling of data often leads to an overlap of data samples between training and testing groups, resulting in skewed experimental evaluations. To circumvent these obstacles, we introduced a groundbreaking solution—the Semi-Shuffle-Pig Detector (SSPD) for PIR datasets. This novel approach ensures a less biased experimental output by maintaining the distinctiveness of testing data samples from the training datasets and systematically discarding superfluous information from raw images. Our optimized method significantly enhances the true performance of classification, providing unbiased experimental evaluations. Remarkably, our approach has led to a substantial 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, therefore, ensures the preservation of pertinent data within the PIR system and eliminates potential biases in experimental evaluations. As a result, it enhances the accuracy and reliability of real-world PIR applications, contributing to improved animal welfare management, elevated food safety standards, and a more sustainable and climate-resilient livestock industry.

Keywords: pig interaction recognition (PIR); convolution neural network (CNN); Xception; ResNet; deep neural network; domestic livestock; pigs (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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