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Somatic cell count detection is the daily work of dairy farms to monitor the health of cows. The feasibility of applying near-infrared spectroscopy to somatic cell count detection was researched in this paper. Milk samples with different somatic cell counts were collected and preprocessing methods were studied. Variable selection algorithm based on hybrid strategy and modelling method based on ensemble learning were explored for somatic cell count detection. Detection model was used to diagnose subclinical mastitis and the results showed that near-infrared spectroscopy could be a tool to realize rapid detection of somatic cell count in milk. [ABSTRACT FROM AUTHOR]

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Simple Summary: Dairy cow behavior detection is of great significance for cattle health management. Through the detection of the four behaviors of dairy cows—standing, lying, eating, and drinking—we can gain valuable insights into the well-being of cows. For example, hoof disease can increase the amount of time a cow lies down, and digestive system issues can cause a decrease in food intake. Visual inspection of cow behavior can keep track of changes in cow behavior, and non-invasive detection can reduce cow discomfort and improve animal welfare. In this study, we employed computer vision-based deep learning techniques for the detection of cow behavior, and experimental results demonstrated its promising application in real farm settings. Dairy cow behavior carries important health information. Timely and accurate detection of behaviors such as drinking, feeding, lying, and standing is meaningful for monitoring individual cows and herd management. In this study, a model called Res-DenseYOLO is proposed for accurately detecting the individual behavior of dairy cows living in cowsheds. Specifically, a dense module was integrated into the backbone network of YOLOv5 to strengthen feature extraction for actual cowshed environments. A CoordAtt attention mechanism and SioU loss function were added to enhance feature learning and training convergence. Multi-scale detection heads were designed to improve small target detection. The model was trained and tested on 5516 images collected from monitoring videos of a dairy cowshed. The experimental results showed that the performance of Res-DenseYOLO proposed in this paper is better than that of Fast-RCNN, SSD, YOLOv4, YOLOv7, and other detection models in terms of precision, recall, and mAP metrics. Specifically, Res-DenseYOLO achieved 94.7% precision, 91.2% recall, and 96.3% mAP, outperforming the baseline YOLOv5 model by 0.7%, 4.2%, and 3.7%, respectively. This research developed a useful solution for real-time and accurate detection of dairy cow behaviors with video monitoring only, providing valuable behavioral data for animal welfare and production management. [ABSTRACT FROM AUTHOR]

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]

To reveal the influence of freeze–thaw cycles (FTCs) on soil carbon and nitrogen changes, six typical soils in Northeast China were selected as the research objects to conduct a FTC simulation test in an artificial climate chamber. Three soil volumetric water contents (10%, 20%, 30%) and eight FTCs (0, 2, 4, 6, 8, 10, 15, 20) were set. The results showed that the soil organic carbon (SOC) and microbial biomass carbon (MBC) contents of different soil types under the FTCs initially exhibited a downward and then an upward trend, while the dissolved organic carbon (DOC) content exhibited an upward and then a downward trend. Otherwise, the fourth and sixth FTCs were the key points of change. The SOC, MBC and DOC contents in paddy fields were higher than those in dry fields, showing upward and then downward trends spatially from northeast to southwest. The SOC and MBC contents in each soil type were the highest at the 20% water content, and the DOC content gradually increased with increasing water content. The ammonium nitrogen (NH4+‐N) content in different soil types at different water contents under the FTCs showed an upward trend first, then a downward trend and finally an upward trend. The NH4+‐N content in paddy fields was higher than that in dry fields. The nitrate nitrogen (NO3‒‐N) content showed a downward trend first, then an upward trend and finally a downward trend. The NO3‒‐N content in dry fields was higher than that in paddy fields. The NH4+‐N contents in the three soil types on the Sanjiang Plain were significantly higher than those on the Songnen Plain. The NH4+‐N and NO3‒‐N contents showed upward trends with increasing water content, but the differences were not significant. The results have implications for the study of different types of soils and provide references for research on the mechanism of soil carbon and nitrogen transformation in typical farming areas in Northeast China. [ABSTRACT FROM AUTHOR]

Simple Summary: Methane and volatile fatty acids are important products of rumen fermentation in dairy cows. Quantitative research on them is of great significance for environmental protection and animal production. The aim of this study was to develop a prediction model using the stacking ensemble learning method and predict the production of rumen fermentation products based on the nutrient level of the diet. The results show that the stacking model has good performance in terms of prediction accuracy. The model proposed in this study can be used as a guideline to optimize diet compositions and improve feeding efficiency. Volatile fatty acids (VFAs) and methane are the main products of rumen fermentation. Quantitative studies of rumen fermentation parameters can be performed using in vitro techniques and machine learning methods. The currently proposed models suffer from poor generalization ability due to the small number of samples. In this study, a prediction model for rumen fermentation parameters (methane, acetic acid (AA), and propionic acid (PA)) of dairy cows is established using the stacking ensemble learning method and in vitro techniques. Four factors related to the nutrient level of total mixed rations (TMRs) are selected as inputs to the model: neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM). The comparison of the prediction results of the stacking model and base learners shows that the stacking ensemble learning method has better prediction results for rumen methane (coefficient of determination ( R 2 ) = 0.928, root mean square error (RMSE) = 0.968 mL/g), AA ( R 2 = 0.888, RMSE = 1.975 mmol/L) and PA ( R 2 = 0.924, RMSE = 0.74 mmol/L). And the stacking model simulates the variation of methane and VFAs in relation to the dietary fiber content. To demonstrate the robustness of the model in the case of small samples, an independent validation experiment was conducted. The stacking model successfully simulated the transition of rumen fermentation type and the change of methane content under different concentrate-to-forage (C:F) ratios of TMR. These results suggest that the rumen fermentation parameter prediction model can be used as a decision-making basis for the optimization of dairy cow diet compositions, rapid screening of methane emission reduction, feed beneficial to dairy cow health, and improvement of feed utilization. [ABSTRACT FROM AUTHOR]

Copyright of Smart Agriculture is the property of Smart Agriculture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A specific variable selection method was proposed based on a three-step hybrid strategy for near-infrared spectral analysis. By analyzing functions of each step and characteristics of various variable selection methods, synergy interval partial least squares, iterative variable subset optimization, and bootstrapping soft shrinkage were chosen for three steps. To test the effect of the three-step hybrid method, it was applied to corn and soil spectral data and compared to other common methods. Results for oil content in corn data showed that the three-step hybrid variable selection method selected 1% variables of full spectrum, calibration determination coefficient, and prediction determination coefficient reached 0.998 and 0.993 where the explained variance was increased by 27.30%. It could effectively extract variables related to the tested substance and provide a new variable selection method for near-infrared spectral analysis. [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]

Individual identification of each cow is significant for precision livestock farming. In this paper, we propose a novel contactless cow identification method based on convolutional neural networks. We first collected a set of side-view images of dairy cows, then employed the YOLO model to detect the cow object in the side-view image, and finally fine-tuned a convolutional neural network model to classify each individual cow. In our experiments, a total of 105 side-view images of cows were collected, and the proposed method achieved an accuracy of 96.65% in cow identification, which outperformed existing experiments. Experimental results demonstrate the effectiveness of the proposed method for cow identification and the potential for our method to be applied to other livestock. [ABSTRACT FROM AUTHOR]

With the development of computer and IT technologies, enterprise management has gradually become information-based management. Moreover, due to poor technical competence and non-uniform management, most breeding enterprises show a lack of organisation in data collection and management. In addition, low levels of efficiency result in increasing production costs. This paper adopts ‘struts2’ in order to construct an information-based management system for standardised and normalised management within the process of production in beef cattle breeding enterprises. We present a radio-frequency identification system by studying multiple-tag anti-collision via a dynamic grouping ALOHA algorithm. This algorithm is based on the existing ALOHA algorithm and uses an improved packet dynamic of this algorithm, which is characterised by a high-throughput rate. This new algorithm can reach a throughput 42% higher than that of the general ALOHA algorithm. With a change in the number of tags, the system throughput is relatively stable. [ABSTRACT FROM AUTHOR]

In Hei Longjiang Province of China, ten million tons of soybean straws are produced every year, most of which are burned or discarded. The comprehensive utilization rate is less than 5 %. However, as a kind of high-quality raw material resources, the soybean straw is the main material when producing fuel ethanol and biodiesel. The traditional chemical detection technology was always applied to test the quality of straws' internal components, which needs long time and high cost. Therefore, this research adopted near-infrared spectroscopy technology to establish models to achieve rapid detection of straw's internal components. One hundred and fifty-two samples of straw were collected within Heilongjiang Province, and the main ingredients, lignin and hemicellulose, were chosen as the research objects. K-stone method is used to divide into calibration and validation sets. The correlation coefficient is adopted to select and validate characteristic bands. After removal of the abnormal sample by concentration of residual method, the emphasis is put on the effect of removal of spectrum noise by applying the wavelet transform. The experimental results are that the model has good stability under the full spectrum band 4000-12,000 cm. After the removal of 3 lignin and 3 hemicellulose samples, respectively, the R of calibration set of lignin and hemicellulose models increased significantly, from 0.5836436 and 0.4994598 to 0.6994097 and 0.6943559, respectively. However, after the 5-layer decomposition of DB2 wavelet, the R increased to 0.8075574 and 0.8214309 separately. The validation set's root mean square error prediction (RMSEP) fell from 0.7738772 and 0.3069899 to 0.5979685 and 0.2761462. After the conversion, the relative standard deviations (RSD) are 1.92 and 1.86 %, respectively. Unknown samples can be real-time predicted in short times after building the model, and the prediction process needs only 1960s. The experiments show that it is feasible to employ near-infrared spectroscopy method for rapid detection of straw components, which provides a new method to detect the straw ingredients rapidly in the future. [ABSTRACT FROM AUTHOR]

Individual identification of dairy cows based on computer vision technology shows strong performance and practicality. Accurate identification of each dairy cow is the prerequisite of artificial intelligence technology applied in smart animal husbandry. While the rump of each dairy cow also has lots of important features, so do the back and head, which are also important for individual recognition. In this paper, we propose a non-contact cow rump identification method based on convolutional neural networks. First, the rump image sequences of the cows while feeding were collected. Then, an object detection model was applied to detect the cow rump object in each frame of image. Finally, a fine-tuned convolutional neural network model was trained to identify cow rumps. An image dataset containing 195 different cows was created to validate the proposed method. The method achieved an identification accuracy of 99.76%, which showed a better performance compared to other related methods and a good potential in the actual production environment of cow husbandry, and the model is light enough to be deployed in an edge-computing device. [ABSTRACT FROM AUTHOR]