Pig Treatment Classification on Thermal Image Data using Deep Learning

Colaco, Savina Jassica; Kim, Junghwan; Poulose, Alwin; Van, Zutphen Sanne; Neethirajan, Suresh; Han, Dong Seog

doi:10.1109/icufn55119.2022.9829713

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…In this section, we describe the various models used in the experiments, including LeNet5 [ 51 ], AlexNet, VGGNet, Xception [ 52 ], CNN-LeakyReLU [ 53 ], CNN-inception, and the proposed DISubNet model. These models are compared for the classification of the pig treatments.…”

Section: Materials and Methodsmentioning

confidence: 99%

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

Colaco

Kim

Poulose

et al. 2023

Animals

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Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96–99.98% classification accuracy with fewer parameters.

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Section: Materials and Methodsmentioning

confidence: 99%

“…The CNN model with LeakyReLU [ 53 ] is a straightforward sequential model consisting of several convolutional layers and a batch normalization layer. Following the convolutional layers is LeakyReLU, which is based on ReLU but has a small slope for negative values rather than a flat slope.…”

Section: Materials and Methodsmentioning

confidence: 99%

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

Colaco

Kim

Poulose

et al. 2023

Animals

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“…Low et al [34] tested CNN-LSTM with a ResNet50 backboned model for full-body pig detection from a top-view camera. Colaco et al [35,36] used the PIR dataset containing thermal images, instead of colored or gray-scaled images, and trained their proposed depth-wise separable designed architecture. These studies demonstrate the wide array of techniques employed in pig interaction recognition, emphasizing the importance of understanding and monitoring pig welfare.…”

Section: Related Workmentioning

confidence: 99%

“…This endeavor draws on diverse methodologies and architectures gleaned from the extant literature. Key among these considerations is the impact of feeding intervals and the manipulation behavior of pen mates on pig behavior [4,18,[22][23][24][25]27,31,35,36]. Restrictive feeding can lead to increased aggression in pigs, resulting in antagonistic social behavior when interacting with other pigs.…”

Section: Related Workmentioning

confidence: 99%

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

Kim,

Poulose,

Colaco

et al. 2023

Agriculture

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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.

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“…Among the existing methods, the development of physiological image sensors and the use of infrared cameras to capture physiological image data have been explored. Additionally, using Convolutional Neural Networks (CNN) to extract deep physiological features with higher accuracy from captured data with fewer parameters [ 19 , 20 ] has become the state of the arts for cough identification [ 21 ]. The development of physiological sensors, however, can be invasive and expensive [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Wang,

Qi,

et al. 2024

PLoS ONE

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Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.

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Pig Treatment Classification on Thermal Image Data using Deep Learning

Cited by 5 publications

References 3 publications

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

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

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

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

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