'Veronika' Is the First Cow Known to Use a Tool This is the first recorded instance of a bovine using tools from her environment to relieve an itch--leaving scientists astonished. Justice for cartoonist Gary Larson: A team of scientists has observed, for the first time, a cow using a tool in a flexible manner. The ingenuity of "Veronika," as the animal is called, shows that cattle possess enough intelligence to manipulate elements of their environment and solve challenges they would otherwise be unable to overcome. Veronika is a pet cow in Austria. Nor was she trained to do tricks; on the contrary, for the past 10 years she has developed the ability to find branches in the grass, choose one, hold it with her mouth, and scratch herself with it to relieve skin irritation. Until now, only chimpanzees had convincingly demonstrated the ability to employ tools to improve their living conditions.

Scientists are rethinking what cattle are capable of after an Austrian cow named Veronika was found to use tools with impressive skill. The discovery, reported by researchers in Vienna, suggests cows may have far greater cognitive abilities than previously assumed. Veronika, a cow living in a mountain village in the Austrian countryside, has spent years perfecting the art of scratching herself using sticks, rakes, and brooms. Word of her behaviour eventually reached animal intelligence specialists in Vienna, who found Veronika used both ends of the same object for different tasks. If it were her back or another tough area that warranted a good scratch, she would use the bristle end of a broom.

Inside cowboy Charles Jesse "Buffalo" Jones's get-rich-quick scheme to restore the plains 100 years ago. By 1888, Charles Jesse "Buffalo" Jones had succeeded in crossbreeding a buffalo with cow, a hybrid he claimed would be as tasty as beef and as hardy as buffalo. Breakthroughs, discoveries, and DIY tips sent every weekday. The "cattalo" was a homely creature--stocky and shaggy, with a slight buffalo's hump and a cow's docile face. Charles "Buffalo" Jones invented the cow-buffalo hybrid in 1888.

This document provides additional details that complement the main paper. We discuss the steps used to synchronize and calibrate the visual data in Section A. Section B elaborates on the details of UWB localization, heading direction estimation, and obtaining the reference for lying behavior. We keep the order of figures, tables, and equations in numerical, and refer to them independently from the main paper unless explicitly stated otherwise. The paper checklist is attached as the final part of the main paper. We discuss additional details of processing the visual data and calibrating four camera views.

This document provides additional details that complement the main paper. We discuss the steps used to synchronize and calibrate the visual data in Section A. Section B elaborates on the details of UWB localization, heading direction estimation, and obtaining the reference for lying behavior. We keep the order of figures, tables, and equations in numerical, and refer to them independently from the main paper unless explicitly stated otherwise. The paper checklist is attached as the final part of the main paper. We discuss additional details of processing the visual data and calibrating four camera views.

Cattle lameness is a prevalent health problem in livestock farming, often resulting from hoof injuries or infections, and severely impacts animal welfare and productivity. Early and accurate detection is critical for minimizing economic losses and ensuring proper treatment. This study proposes a spatiotemporal deep learning framework for automated cattle lameness detection using publicly available video data. We curate and publicly release a balanced set of 50 online video clips featuring 42 individual cattle, recorded from multiple viewpoints in both indoor and outdoor environments. The videos were categorized into lame and non-lame classes based on visual gait characteristics and metadata descriptions. After applying data augmentation techniques to enhance generalization, two deep learning architectures were trained and evaluated: 3D Convolutional Neural Networks (3D CNN) and Convolutional Long-Short-Term Memory (ConvLSTM2D). The 3D CNN achieved a video-level classification accuracy of 90%, with a precision, recall, and F1 score of 90.9% each, outperforming the ConvLSTM2D model, which achieved 85% accuracy. Unlike conventional approaches that rely on multistage pipelines involving object detection and pose estimation, this study demonstrates the effectiveness of a direct end-to-end video classification approach. Compared with the best end-to-end prior method (C3D-ConvLSTM, 90.3%), our model achieves comparable accuracy while eliminating pose estimation pre-processing.The results indicate that deep learning models can successfully extract and learn spatio-temporal features from various video sources, enabling scalable and efficient cattle lameness detection in real-world farm settings.

Forecasting mob based cattle weight gain (MB CWG) may benefit large livestock farms, allowing farmers to refine their feeding strategies, make educated breeding choices, and reduce risks linked to climate variability and market fluctuations. In this paper, a novel technique termed MB CWG is proposed to forecast the one month advanced weight gain of herd based cattle using historical data collected from the Charles Sturt University Farm. This research employs a Random Forest (RF) model, comparing its performance against Support Vector Regression (SVR) and Long Short Term Memory (LSTM) models for monthly weight gain prediction. Four datasets were used to evaluate the performance of models, using 756 sample data from 108 herd-based cattle, along with weather data (rainfall and temperature) influencing CWG. The RF model performs better than the SVR and LSTM models across all datasets, achieving an R^2 of 0.973, RMSE of 0.040, and MAE of 0.033 when both weather and age factors were included. The results indicate that including both weather and age factors significantly improves the accuracy of weight gain predictions, with the RF model outperforming the SVR and LSTM models in all scenarios. These findings demonstrate the potential of RF as a robust tool for forecasting cattle weight gain in variable conditions, highlighting the influence of age and climatic factors on herd based weight trends. This study has also developed an innovative automated pre processing tool to generate a benchmark dataset for MB CWG predictive models. The tool is publicly available on GitHub and can assist in preparing datasets for current and future analytical research..

This paper presents a novel system for monitoring cattle behavior and detecting estrus (heat) periods using sensor data and machine learning. We designed and deployed a low-cost Bluetooth-based neck collar equipped with accelerometer and gyroscope sensors to capture real-time behavioral data from real cows, which was synced to the cloud. A labeled dataset was created using synchronized CCTV footage to annotate behaviors such as feeding, rumination, lying, and others. We evaluated multiple machine learning models -- Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) -- for behavior classification. Additionally, we implemented a Long Short-Term Memory (LSTM) model for estrus detection using behavioral patterns and anomaly detection. Our system achieved over 93% behavior classification accuracy and 96% estrus detection accuracy on a limited test set. The approach offers a scalable and accessible solution for precision livestock monitoring, especially in resource-constrained environments.