Discrimination strategy using machine learning technique for oestrus detection in dairy cows by a dual-channel-based acoustic tag.

• A neck tag equipped with low-cost dual-channel microphones and a multi-threshold-based sound extraction method were developed to effectively collect vocalisations of individual cows. • A series of statistical methods consisting of significance analysis, correlation analysis, and individual difference analysis were adopted to evaluate the availability of acoustic and statistical features of dairy cow vocalisations for heat detection. • A dual-LSTM joint discriminant strategy based on optimal parameters was proposed to achieve the identification of oestrus state and the estimation of oestrus onset. Timely identification of cows in heat is a fundamental issue in dairy farming. Although current studies have confirmed that several cow vocalisation characteristics can be used for oestrus identification, the reliability and practicability of oestrus detection methods are limited due to background noise, a lack of data support for sound feature selection, and the unsatisfactory effectiveness of vocalisation recognition algorithms. To overcome these predicaments, a dual-channel recording device and a sound event extraction method were developed in this study. The experimental results in respect of 62 Holstein dairy cows showed that the sound sample recording accuracy was 80.4%, and the noise filtering accuracy was 94.3%, indicating the effectiveness of the vocalisation extraction algorithm in a noisy farm environment. Moreover, two unique sound features with remarkable discrimination ability, i.e., the number of consecutive vocalisations and the maximum consecutive times, were used to improve the effectiveness of oestrus detection. The Friedman test, the Spearman rank order correlation coefficient, and the Kruskal-Wallis test were used to obtain the combination of features with the most optimal discrimination. Subsequently, a dual-LSTM (Long Short-Term Memory) joint discriminant strategy based on optimal combinations was proposed to promote oestrus detection performance. The results of the blind test on the vocalisation data of 20 oestrus cows and 11 non-oestrus cows demonstrated that the oestrus detection rate of the discrimination strategy reached 100%, and had a temporal advantage over the activity-index-based method in determining the onset of oestrus. The improvements of the selection and-based oestrus detection method mainly included accurately extracting individual cow vocalisations, supplementing the explicability of feature selection, and enhancing the refinement of oestrus recognition. Meanwhile, it is of positive significance for applying sound acquisition devices in oestrus detection. Furthermore, combining sound identification with other automated detection techniques appears to be a promising method for promoting the oestrus detection rate. [ABSTRACT FROM AUTHOR]