AIMC Topic: Cattle

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Monitoring of milking routines for dairy cows using a computer vision system: A diagnostic accuracy study.

Journal of dairy science
The primary objective was to assess the performance of a computer vision system for the detection of reattachment and manual removal of the milking unit, as well as the assessment of the preparation lag time of the milking routine. The secondary obje...

Leveraging unsupervised machine learning techniques for detecting outliers in the daily milk yield data of dairy cows.

Journal of dairy science
The lactation curve is essential for developing effective feeding plans, optimizing breeding, and strategizing milk production for dairy farms. However, health disorders, as well as external factors such as heat stress, dietary changes, and certain m...

Association of artificial intelligence-predicted milk yield residuals to behavioral patterns and transition success in multiparous dairy cows.

Journal of dairy science
Data-driven health monitoring based on milk yield has shown potential to identify health-perturbing events during the transition period. As a proof of principle, we explored the association between the cow's residual milk yield, that is, the differen...

Exploration of the fluorine-fluorine interaction mechanism in fluoroquinolone antibiotics recognition and ciprofloxacin detection on the basis of fluorine-doped carbon quantum dots and machine learning.

Food chemistry
The uncontrolled use of antibiotics poses a significant threat to human health and ecosystems. Accurate differentiation and trace detection of fluoroquinolone antibiotics (FQs) in foods are imperative. Fluorine-doped carbon quantum dots chelated with...

Model-driven multivariate control chart and support vector machine as tools to detect variation in the milking process and monitor parlor performance.

Journal of dairy science
The efficiency of the milking process is the key to dairy farm management. However, due to the high variability of data from single or multiple milk meters, it is difficult to know whether the milking process is under control or not. The main objecti...

Detection and quantification of formaldehyde adulteration in cow and buffalo milk using UV-Vis-NIR spectroscopy with machine learning.

Food chemistry
This work uses UV-Vis-NIR spectroscopy (200-1700 nm), spectral preprocessing, principal component analysis (PCA), and machine learning (ML) to identify and quantify formalin adulteration in cow and buffalo milk. Formalin was added to milk at various ...

Predicting dyscalcemia status in early-lactation multiparous Holstein cows using milk weight and constituent analysis from a single milking at 4 days in milk.

Journal of dairy science
Many multiparous cows struggle to adapt to the challenges of the early postpartum period. Dyscalcemia, a condition defined by low blood calcium concentrations at 4 DIM and associated with suboptimal performance across a spectrum of epidemiologically ...

Artificial intelligence outperforms humans in morphology-based oocyte selection in cattle.

Scientific reports
Evaluating cumulus-oocyte complex (COC) morphology is commonly used to assess oocyte quality. However, clear guidelines on interpreting COC morphology data are lacking as this evaluation method is subjective. In the present study, individual in vitro...

Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.

Scientific reports
In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen and proteoglycans, mimicking an osteoarthritis condition in ex-vivo bovine cartilage samples. Most ana...

Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis.

Scientific reports
For modeling dairy cattle data, fuzzy logic offers the capability to manage uncertainty, enhance accuracy, facilitate informed decision-making, and optimize resource allocation. A critical aspect of dairy cattle production is the modeling of mastitis...