Research on the Intelligent Cloud Animal Husbandry Platform

Wang, Buyu; Qi, Jingwei

doi:10.1155/2022/4940511

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…Importantly, each mp4 file contains audio data and generates new audio and video files every 10 minutes. To facilitate synchronized data transfer between the Edge server of the pig farm and the Storage server of the cloud animal husbandry platform [ 28 ], a robust Internet connection links the two. The Rsync file synchronization protocol plays a pivotal role in automatically and incrementally transferring audio and video files from the pig farm to the cloud animal husbandry platform.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%