Identification and classification for sheep foraging behavior based on acoustic signal and deep learning

Abstract

It is very significant to monitor livestock foraging behavior accurately and in real-time to further improve pasture management and livestock welfare. Although various algorithms have been developed to identify and classify animals' foraging behavior, it still has room to be improved in generality and function. In this study, a representative acoustic dataset generated by typical bodyweight sheep when grazing on various grasses and subsequent ruminating was created. Then an algorithm for the identification and classification of foraging behavior was proposed based on feature extraction technique and deep learning. Specifically, all prominent fragments in the acoustic signal were identified as events by the identification algorithm, and the events were classified as noise, chew, bite, chew-bite, or ruminating behavior through the classification model. The effect of each parameter on the identification algorithm was analyzed, and an optimal set of parameters was derived. As a result, the accuracy of 96.13% was achieved by the identification algorithm with the optimal parameters. Meanwhile, the performances of three common deep network models, including deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN), were compared. The results showed that the RNN, CNN, and DNN model's accuracy were 93.17%, 92.53%, and 79.43%, respectively. The RNN model had a stronger classification capacity than the CNN model since it involved the inter-dependent information of adjacent events. The CNN model achieved a superior classification performance than the DNN model because the log-scaled Mel-spectrogram representation of the event waveform was more effective than the waveform itself. The algorithm proposed in this study could be well applied to identify and classify all foraging behaviors of typical weight sheep foraging freely on various grasses in the future.