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DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data.

Savina Jassica Colaco1, Jung Hwan Kim1, Alwin Poulose2

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Animals : an Open Access Journal From MDPI
|April 13, 2023
PubMed
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This summary is machine-generated.

Thermal imaging aids pig welfare by classifying treatments. New lightweight deep learning models, DISubNetV1 and DISubNetV2, achieve over 99.9% accuracy in identifying pig treatments from thermal images.

Area of Science:

  • Animal Science
  • Computer Vision
  • Machine Learning

Background:

  • Early detection of health and welfare issues in intensive pig production is critical.
  • Thermal imaging offers a non-invasive method for monitoring animal health.
  • Classifying pig treatments using thermal imaging can enhance animal welfare and sustainability.

Purpose of the Study:

  • To develop and evaluate lightweight deep learning models for classifying pig treatments using thermal imaging.
  • To compare the performance of proposed models against existing deep learning architectures.
  • To demonstrate the effectiveness of thermal imaging in improving pig welfare management.

Main Methods:

  • Introduction of a novel depthwise separable inception subnetwork (DISubNet) architecture.
Keywords:
animal welfaredepthwise separable layerimage classificationinceptionthermal data

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  • Development of two DISubNet versions: DISubNetV1 and DISubNetV2.
  • Training and evaluation of models using a thermal dataset captured by a forward-looking infrared (FLIR) camera.

    • Main Results:

    • The proposed DISubNet models achieved superior performance in classifying pig treatments compared to other deep learning models.
    • Both DISubNetV1 and DISubNetV2 demonstrated high classification accuracy, ranging from 99.96% to 99.98%.
    • The models achieved high accuracy with a significantly reduced number of parameters, indicating efficiency.

    Conclusions:

    • The developed DISubNet models are highly effective for pig treatment classification using thermal imaging.
    • These models contribute to improved animal welfare and sustainable practices in pig production.
    • Lightweight deep learning models show great promise for real-time health monitoring in livestock.