AIMC Topic: Livestock

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A Novel Dual-Network Approach for Real-Time Liveweight Estimation in Precision Livestock Management.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The increasing demand for automation in livestock farming scenarios highlights the need for effective noncontact measurement methods. The current methods typically require either fixed postures and specific positions of the target animals or high com...

Interval price prediction of livestock product based on fuzzy mathematics and improved LSTM.

PloS one
Livestock product prices serve as a barometer and bellwether for the agricultural market. However, traditional point prediction techniques focus mainly on tracking or fitting, resulting in limited information and challenges in evaluating the uncertai...

Design optimization of bimetal-modified biochar for enhanced phosphate removal performance in livestock wastewater using machine learning.

Bioresource technology
Mg-modified biochar shows high adsorption performance under weakly acidic and neutral water conditions. However, its phosphate removal efficiency markedly decreases in naturally alkaline wastewater, such as that released in livestock farming (anaerob...

A Systematic Review and Meta-Analysis of the Efficacy of Biosecurity in Disease Prevention and Control in Livestock Farms in Africa.

Transboundary and emerging diseases
In Africa, livestock production plays a crucial role for sustainable food security and economic growth. However, the development of this sector has been delayed by livestock diseases, one of the main constraints, which can cause important production ...

Unveiling livestock trade trends: A beginner's guide to generative AI-powered visualization.

Research in veterinary science
This tutorial, rooted in the context of livestock research, is designed to assist novice or non-programmers in visualizing trends in livestock exports between the US and Japan using Python and generative AI systems such as Microsoft's Copilot and Goo...

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics.

Omics : a journal of integrative biology
Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the ...

Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock.

Sensors (Basel, Switzerland)
Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal pe...

Puzzle: taking livestock tracking to the next level.

Scientific reports
Animal behavior is a critical aspect for a better understanding and management of animal health and welfare. The combination of cameras with artificial intelligence holds significant potential, particularly as it eliminates the need to handle animals...

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus.

Environmental pollution (Barking, Essex : 1987)
This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning...

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock.

Computational biology and chemistry
A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock speci...