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Simple Summary: Our objective is to establish an in vitro rumen fermentation model that can dynamically simulate the fermentation process of various total mixed ration (TMR) diets in the rumen of dairy cows, enabling a quantitative investigation of rumen methane and rumen acetic acid concentrations. The models were assessed for their prediction accuracy and precision, while the independent verification experiments confirmed the models' generalization ability across different total mixed ration (TMR) ratios (C:F). These results show that in vitro rumen models constructed by machine learning methods can be used as a tool to quantify rumen fermentation parameters (methane and acetic acid) and guide the dietary structure optimization of dairy cows. The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency. [ABSTRACT FROM AUTHOR]

The precise detection of pig cough is a crucial step for establishing an early warning system for pig respiratory diseases. With regard to high precision pig cough recognition, feature extraction and selection are of importance. However, few studies have investigated both acoustic and visual features of pig vocalisations as input features. In this paper, we proposed a novel feature fusion method which fusing acoustic and visual features to achieve an enhanced pig cough recognition rate. We firstly extracted acoustic features from audio signals, including root-mean-square energy (RMS), mel-frequency cepstral coefficients (MFCCs), zero-crossing rates (ZCRs), spectral centroid, spectral roll-off, spectral flatness, spectral bandwidth and chroma. Then, constant-Q transform (CQT) spectrograms were employed to extract visual features involving local binary pattern (LBP) and histogram of gradient (HOG). Subsequently, a hybrid feature set was created by combining acoustic and visual features. In this stage, Pearson correlation coefficient (PCC), recursive feature elimination based on random forest (RF-RFE) and principal component analysis (PCA) were exploited for dimensionality reduction. Finally, support vector machine (SVM), random forest (RF) and k-nearest neighbours (KNN) classifiers were used to conduct a performance evaluation. It is shown that the fused acoustic features (Acoustic) combined with LBP and HOG (A-LH) achieved 96.45% pig cough accuracy. The results reveal that the fusion feature set outperforms acoustic and visual features alone. [Display omitted] • Both acoustic and visual features were investigated for pig cough. • A hybrid feature vector created by early feature fusion enhanced recognition performance of pig cough. • Feature selection and extraction were employed to choose the optimal feature set. • The proposed method reached an accuracy of 96.45%. [ABSTRACT FROM AUTHOR]

Detection of additives in milk is important, especially preservatives. Potassium sorbate was taken as an example to study the feasibility of hyperspectral imaging as a rapid detection tool for preservatives in milk. Samples with different concentrations of potassium sorbate were divided into four groups. Pretreatment and variable selection methods were studied based on the support vector machine model. The model with standard normalized variable pretreatment and competitive adaptive reweighted sampling variable selection had the best performance. Testing set accuracy for different groups from low concentration to high concentration were 92.16%, 95.7%, 99.46%, and 98.33%. Hyperspectral imaging can be a rapid detection tool for potassium sorbate in milk. [ABSTRACT FROM AUTHOR]

• Both acoustic and deep features were investigated for pig cough. • A novel acoustic and deep feature fusion framework for pig cough recognition was proposed. • The proposed method reached an accuracy of 97.35%. The recognition of pig cough sound is a prerequisite for early warning of respiratory diseases in pig houses, which is essential for detecting animal welfare and predicting productivity. With respect to pig cough recognition, it is a highly crucial step to create representative pig sound characteristics. To this end, this paper proposed a feature fusion method by combining acoustic and deep features from audio segments. First, a set of acoustic features from different domains were extracted from sound signals, and recursive feature elimination based on random forest (RF-RFE) was adopted to conduct feature selection. Second, time-frequency representations (TFRs) involving constant-Q transform (CQT) and short-time Fourier transform (STFT) were employed to extract visual features from a fine-tuned convolutional neural network (CNN) model. Finally, the ensemble of the two kinds of features was fed into support vector machine (SVM) by early fusion to identify pig cough sounds. This work investigated the performance of the proposed acoustic and deep features fusion, which achieved 97.35% accuracy for pig cough recognition. The results provide further evidence for the effectiveness of combining acoustic and deep spectrum features as a robust feature representation for pig cough recognition. [ABSTRACT FROM AUTHOR]

Livestock identification is of great significance for achieving precision livestock farming as it is a prerequisite of modern livestock management and automatic behaviour analysis. With respect to cow identification, methods based on computer vision have been widely considered due to their advantage of non-contact and practicality. In this paper, a novel non-contact cow identification method based on fusion of deep parts features is proposed. First, a set of side-view images of cows were captured, and then the YOLO object detection model was applied to locate the cow object in each original image, which was then divided into three parts, head, trunk and legs, by a part segmentation algorithm using frame differencing and segmentation span analysis. Then, three independent convolutional neural networks (CNNs) were trained to extract deep features from these three parts, and a feature fusion strategy was designed to fuse the features, i.e., deep parts feature fusion. Finally, a support vector machine (SVM) classifier trained by the fused features was used to identify each individual cow. The proposed method achieved 98.36% cow identification accuracy on a dataset containing side-view images of 93 cows, which outperformed existing works. Experimental results showed the effectiveness of the proposed cow identification method and the good potential for this method in individual identification of other livestock. Image 1 • Extracts and fuses discriminative deep cow-part features for cow identification. • Optimal combinations of cow part features found for individual cow identification. • 98.36% accuracy reached, outperforming existing works on the side-view images of 93 cows. [ABSTRACT FROM AUTHOR]

• Proposed universal digital twin architecture for dairy cows. • Implementation of a low-cost, easy-to-implement digital twin solution for dairy cows. • Development of a new collar, anchor point, and software, demonstrated through effective feeding behavior classification. • Accuracy of recognition for region-specific behavior improved by combining indoor positioning data with IMU data. The digital twin of cows holds significant promise for advancing animal welfare and production efficiency. This paper aims to propose an architecture for digital twins of cows that covers their entire lifecycle. A digital twin solution has been developed that utilizes indoor positioning data and inertial measurement unit (IMU) data to construct a cow's digital shadow. As an example, the paper utilizes the classification of cow feeding and non-feeding behaviors to study the digital twin perception and modeling methods. A custom-made collar integrated with utilized ultra-wideband (UWB) chips and inertial measurement units (IMUs) was utilized to collect real-time location and neck movement data from five healthy non-lactating Holstein cows. The collected data was transmitted via UWB signals to the positioning anchor and subsequently forwarded to a local server. To classify the feeding and non-feeding behaviors of the cows, three methods were employed: Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM). According to the experimental results, all three classification methods were effective, however, LSTM outperformed the others. Employing solely IMU data and implementing the LSTM, the precision of identifying bovine foraging behavior reached 91.05%, with concomitant precision and recall rates of 92.23 and 91.35%, respectively. Through an integration of the data from indoor position detection and IMU devices and the employment of LSTM, the accuracy of identification increased to 94.97%, with a precision rate of 99.99% and a recall rate of 93.86%. The trial of the digital twin solution demonstrated the rationality and technical feasibility of the digital twin architecture, which holds significant reference value for the development of animal digital twins in the animal husbandry industry. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is an important task in remote sensing image analysis. Traditional machine learning techniques are difficult to deal with hyperspectral images directly, because hyperspectral images have too many redundant spectral channels. In this paper we propose a novel method for hyperspectral image classification, by which spectral and spatial features are jointly exploited from hyperspectral images. Firstly, considering the local similarity in spatial domain, we employ a large spatial window to get image blocks from hyperspectral image Secondly, each spectral channel of the image block is filtered to extract their spatial and spectral features, after that the features are merged by convolutional layers. Finally, the fully-connected layers are used to get the classification result. Comparing with other state-of-the-art techniques, the proposed method pays more attention to the correlation of spatial neighborhood by using a large spatial window in the network. In addition, we combine the proposed network with the traditional support vector machine (SVM) classifier to improve the performance of hyperspectral image classification. Moreover, an adaptive method of the spatial window sizes selection is proposed in this paper. Experimental results conducted on the AVIRIS and ROSIS datasets demonstrate that the proposed method outperforms the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

• We proposed two fusion strategies to promote the accuracy of pig cough recognition. • Classifier fusion was proven to outperform feature fusion. • The proposed method achieved an accuracy of 99.20%. The recognition of pig cough sounds is an effective way to monitor pig respiratory diseases which seriously affect healthy pig breeding. Due to the complexity of the pig-housing environment, achieving high precision cough recognition by relying only on a single feature or classifier is challenging. Therefore, in this study we investigated two fusion strategies, namely feature fusion and classifier fusion, to boost classification accuracy. For feature fusion, we improved the previously proposed feature fusion algorithm and selected better acoustic and image features for fusion. We also proposed a novel classifier fusion algorithm. In the algorithm, the support vector machine (SVM) classifiers trained by the acoustic features and deep features were fused by soft voting for pig cough prediction. The sound data collected in the pig barn were used to validate the proposed methods. Our methods achieved a substantial classification rate of 97.47% and 99.20% for feature fusion and classifier fusion, respectively. The results demonstrate that our proposed fusion strategies can significantly improve the recognition accuracy of pig cough sounds. [ABSTRACT FROM AUTHOR]