The safe use of pharmaceuticals in food-producing animals is vital to protect animal welfare and human food safety. Adverse events (AEs) may signal unexpected pharmacokinetic or toxicokinetic effects, increasing the risk of violative residues in the food chain. This study introduces a predictive framework for classifying outcomes (Death vs. Recovery) using ~1.28 million reports (1987-2025 Q1) from the U.S. FDA's OpenFDA Center for Veterinary Medicine. A preprocessing pipeline merged relational tables and standardized AEs through VeDDRA ontologies. Data were normalized, missing values imputed, and high-cardinality features reduced; physicochemical drug properties were integrated to capture chemical-residue links. We evaluated supervised models, including Random Forest, CatBoost, XGBoost, ExcelFormer, and large language models (Gemma 3-27B, Phi 3-12B). Class imbalance was addressed, such as undersampling and oversampling, with a focus on prioritizing recall for fatal outcomes. Ensemble methods(Voting, Stacking) and CatBoost performed best, achieving precision, recall, and F1-scores of 0.95. Incorporating Average Uncertainty Margin (AUM)-based pseudo-labeling of uncertain cases improved minority-class detection, particularly in ExcelFormer and XGBoost. Interpretability via SHAP identified biologically plausible predictors, including lung, heart, and bronchial disorders, animal demographics, and drug physicochemical properties. These features were strongly linked to fatal outcomes. Overall, the framework shows that combining rigorous data engineering, advanced machine learning, and explainable AI enables accurate, interpretable predictions of veterinary safety outcomes. The approach supports FARAD's mission by enabling early detection of high-risk drug-event profiles, strengthening residue risk assessment, and informing regulatory and clinical decision-making.

Greenhouse gas emissions are a key driver of climate change. We asked Erica about her work, and her experience at the AfriClimate AI workshop at the Deep Learning Indaba, where her research won an award. I am Erica Kimei, a PhD candidate at the Nelson Mandela African Institution of Science and Technology in Tanzania (NM-AIST), and an assistant lecturer at the National Institute of Transport. My research focuses on leveraging machine learning and remote sensing technology to monitor and forecast greenhouse gas emissions from ruminant livestock. This work aims to contribute to sustainable agricultural practices by enabling better management of emissions and addressing the climate impacts of livestock farming.

The police chief of a small Nebraska city has come forward with a warning for New Jersey after his community was terrorized by mystery drones. Ord, Nebraska Police Chief Chris Grooms revealed to DailyMail.com Across nearly three weeks of nighttime encounters, typically between 7pm and 11pm, these inexplicable SUV-sized drones operated'with impunity,' Chief Grooms said, and sometimes seemed to be'toying with law enforcement.' 'A lot of reports by ranchers stated that these objects were harassing their horses or cattle on a nightly basis,' he added. Some of the drones reached speeds of 120mph.

Precision livestock farming (PLF) aims to improve the health and welfare of livestock animals and farming outcomes through the use of advanced technologies. Computer vision, combined with recent advances in machine learning and deep learning artificial intelligence approaches, offers a possible solution to the PLF ideal of 24/7 livestock monitoring that helps facilitate early detection of animal health and welfare issues. However, a significant number of livestock species are raised in large outdoor habitats that pose technological challenges for computer vision approaches. This review provides a comprehensive overview of computer vision methods and open challenges in outdoor animal monitoring. We include research from both the livestock and wildlife fields in the review because of the similarities in appearance, behaviour, and habitat for many livestock and wildlife. We focus on large terrestrial mammals, such as cattle, horses, deer, goats, sheep, koalas, giraffes, and elephants. We use an image processing pipeline to frame our discussion and highlight the current capabilities and open technical challenges at each stage of the pipeline. The review found a clear trend towards the use of deep learning approaches for animal detection, counting, and multi-species classification. We discuss in detail the applicability of current vision-based methods to PLF contexts and promising directions for future research.

Yamagata University said Tuesday it has newly identified 303 Nazca Lines geoglyphs in southern Peru. The national university, which has a research institute focused on the World Heritage drawings, added that the Nazca Lines are highly likely to have been created for the purposes of rituals and information sharing. The findings were published in the U.S. journal Proceedings of the National Academy of Sciences the same day. Yamagata University identified the geoglyphs using artificial intelligence technology in cooperation with IBM Research of the United States. Researchers found the drawings through field surveys conducted from September 2022 and February 2023 of sites selected with the AI technology from aerial photographs.

Technology-driven precision livestock farming (PLF) empowers practitioners to monitor and analyze animal growth and health conditions for improved productivity and welfare. Computer vision (CV) is indispensable in PLF by using cameras and computer algorithms to supplement or supersede manual efforts for livestock data acquisition. Data availability is crucial for developing innovative monitoring and analysis systems through artificial intelligence-based techniques. However, data curation processes are tedious, time-consuming, and resource intensive. This study presents the first systematic survey of publicly available livestock CV datasets (https://github.com/Anil-Bhujel/Public-Computer-Vision-Dataset-A-Systematic-Survey). Among 58 public datasets identified and analyzed, encompassing different species of livestock, almost half of them are for cattle, followed by swine, poultry, and other animals. Individual animal detection and color imaging are the dominant application and imaging modality for livestock. The characteristics and baseline applications of the datasets are discussed, emphasizing the implications for animal welfare advocates. Challenges and opportunities are also discussed to inspire further efforts in developing livestock CV datasets. This study highlights that the limited quantity of high-quality annotated datasets collected from diverse environments, animals, and applications, the absence of contextual metadata, are a real bottleneck in PLF.

At a startup farm outside of Oakland, a young man reads on his phone in a dim bedroom. He's not scrolling through social-media feeds or playing games; he's trying to learn about caring for his livestock. A Siri-like voice-over says, "I found one article on how to take care of baby chicks." On the floor, a large blue bucket sits under the warm glow of a heat lamp, with about a dozen fluffy chicks inside. "Failure to maintain a warm environment will quickly prove to be fatal," the digital voice explains.

With over 7.9 billion humans, Extensive research has been performed in the field of machine it is getting harder for the majority of the population learning for social science to discover new findings, to lead a healthy life. Around 9.9% of the population, which understand the causal effects, and make predictions. Scholars accounts for 811 million people, still go to bed on an empty have experimented with various traditional mathematical stomach. On the contrary, over 1.3 billion tonnes of food are models, machine learning models and deep learning wasted every year. The world's population is rapidly growing, methods for food demand forecasting. Some of the popular and it is estimated that there will be around 10 billion choices include ARIMA, Holt-Winters, supervised regression people on Earth by the year 2050. Environmentalists have models, and artificial neural networks like NARXNN been trying to find solutions to reduce the numbers in terms (non-linear auto regressive exogenous neural network). of hunger and food wastage. Sustainable food development The research (Lutoslawski et al. 2021) uses a nonlinear ensures that the current and future human population has autoregressive neural network for food demand prediction.

In a potential breaththrough for monitoring animal wellbeing, scientists say they have translated pig grunts into emotions for the first time. Researchers trained an artificial intelligence (AI) algorithm with 7,414 recordings of pig noises, gathered throughout the life stages of 411 pigs – including slaughter. The algorithm could potentially be used to build an app for pig farmers that detects whether the animals are happy just from the noise they're making. With enough data to train the algorithm, the method could also be used to better understand the emotions of other mammals, experts say. This image shows the classification of pig calls to'valence and context', based on the algorithm. The research was led by the University of Copenhagen, the ETH Zurich and the France's National Research Institute for Agriculture, Food and Environment (INRAE).

Technological advances are bringing change to a great number of industries, and the agriculture industry is no exception. Farms are slowly starting to see increased adoption of practices based on technologies such as artificial intelligence, cloud computing, the Internet of Things (IoT), and robotics. The adoption of such technologies into the traditional farming practices as we know them is referred to as smart farming or farm automation. Let's have a look at what farm automation is exactly and how it can help farmers tackle a number of challenges in today's agricultural sector. Farm automation specifically focuses on applying data and information technologies for the optimization of production processes of complex farming systems as well as the quality of the food.