Yu Youxin, Zhu Wenbo, Ma Xiaoli, Du Jialei, Liu Yu, Gan Linhui, An Xiaoping, Li Honghui, Wang Buyu, Fu Xueliang
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Hohhot 010018, China.
Animals (Basel). 2024 Nov 13;14(22):3267. doi: 10.3390/ani14223267.
In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms' deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior.
在精准饲养中，对绵羊采食行为进行非接触、无压力监测对于健康监测和优化生产管理至关重要。使用声学传感器识别绵羊采食行为时，实验条件与实际环境存在差异，这会导致差异，进而给在复杂生产环境中实现高精度分类带来挑战。本研究通过整合与采食行为相关的深度频谱图特征和声学特征来提高分类性能。我们在实际生产环境中进行了声音数据收集任务，考虑到了噪声和复杂的环境。该方法包括评估和筛选最佳声学特征，利用定制的卷积神经网络（SheepVGG-Lite）提取短时傅里叶变换（STFT）频谱图和恒定Q变换（CQT）频谱图的深度特征，采用互谱图特征融合，并通过支持向量机（SVM）评估分类性能。结果表明，互谱特征融合显著提高了分类性能，分类准确率达到了96.47%。这些发现凸显了将声学特征与频谱图深度特征相结合以准确识别绵羊采食行为的价值。
