Identification of grass growth conditions based on sheep grazing acoustic signals
Identification of grass growth conditions based on sheep grazing acoustic signals
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DOI：
10.1016/j.compag.2021.106463
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发表时间：
2021-09-25
影响因子：
8.3
通讯作者：
Ma, YanHua
中科院分区：
文献类型：
--
作者：
Wang, Kui;Wu, Pei;Ma, YanHua
羊吃草的声音信号携带着丰富的信息。迄今为止，关于放牧行为和声音信号摄入的信息已被彻底探索和利用。然而，有关草生长条件（草状况）的信息却很少受到学者的关注，尽管它对于轮牧决策至关重要。本研究试图有效地挖掘和处理声学信号中的草地状况信息，以获得高性能的识别模型。首先，将三种草地条件下采集的声学信号分为许多分段样本。其次，根据每个分段样本构建了六种类型的正式样本。最后，将六个正式样本的 log-Mel 特征分别输入到卷积神经网络（CNN）模型或递归神经网络（RNN）模型中以识别草地状况。在将log-Mel特征输入模型之前，使用统一样本长度和多个指定长度两种方法对正式样本进行预处理。结果表明，固定咀嚼和咬合连接（FCB）样本与CNN模型的组合表现最好，准确率达到90.24%。当指定长度长于 8 s 时，填充或截断样本波形的方法往往比缩放样本的 log-Mel 特征更好。不同的指定长度对填充方法模型的精度有重要影响。本研究的应用可为构建更加合理的轮牧策略提供数据参考。
The acoustic signals of sheep grazing carry a wealth of information. To date, the information on grazing behavior and intake in acoustic signals has been thoroughly explored and utilized. However, information on grass growth conditions (grass conditions) has received little attention from scholars, although it is crucial for making rotational grazing decisions. This study tries to efficiently mine and process the grass condition information in acoustic signals to obtain a high-performing recognition model. First, the acoustic signals collected under three grass conditions were divided into many segment samples. Second, six types of formal samples were constructed from every segment sample. Last, the log-Mel features of the six formal samples were separately fed into a convolutional neural network (CNN) model or a recurrent neural network (RNN) model to identify the grass conditions. Before the log-Mel features were fed into the model, two methods of unifying the sample length and multiple specified lengths were used to pre-process the formal samples. The results showed that the combination of the fixed chewing and biting connection (FCB) sample and the CNN model performed the best, with an accuracy of 90.24%. The method of filling or truncating the sample's waveform tended to be better than scaling the sample's log-Mel feature when the specified length was longer than 8 s. The different specified lengths had an essential effect on the accuracy of the model with the filling method. The application of this study could provide a data reference for constructing a more rational rotational grazing strategy.