Artificial intelligence in veterinary and animal science: applications, challenges, and future prospects

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110395 - 110395

Published: April 16, 2025

Language: Английский

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Enhancing Inference Capability in Rule-Based Expert Systems for Disease Diagnosis: Advanced Rule Promotion Methodology

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 337 - 346

Published: Jan. 1, 2025

Language: Английский

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Redefining IoT and AI Transformations in Livestock Farming

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 251 - 266

Published: March 14, 2025

IoT and AI technologies continue to rapidly develop change the way many industries, including agriculture, veterinary fishery operate. Agriculture has also incorporated several such as robotics, nanotechnology, synthetic protein gene editing in its traditional farming system. The technology mash-up holds essential value increasing efficiency driving a more sustainable, ecological agriculture. As world continues enter into this time, it is becoming clear that new solutions have turned up bringing revolution with IOT Livestock Management by giving fresh of facing these problems which then already faced for decades. This chapter provides an overview broad space orchids land examples livestock transformations.

Language: Английский

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Integrating Tradition and Innovation

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 287 - 310

Published: May 8, 2025

This chapter examines the integration of Indigenous Technical Knowledge (ITK) with modern agricultural practices to promote sustainability, productivity, and resilience in farming systems. Rooted centuries adaptation local environments, ITK encompasses diverse methods such as water harvesting, soil health management, animal practices, biodiversity conservation. Highlighting like khadin system, johads, panchagavya, sacred groves, this showcases ITK's relevance addressing contemporary challenges. It explores potential combining advanced technologies Internet Things (IoT), Geographic Information Systems (GIS), Machine Learning (ML) optimize resource use, improve fertility, conserve biodiversity. The addresses challenges merging traditional wisdom innovations, emphasizing a vital strategy for sustainability face climate variability constraints.

Language: Английский

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Application of Artificial Intelligence in Agri-Tech, Environmental and Biodiversity Conservation

Array, Journal Year: 2025, Volume and Issue: unknown, P. 100412 - 100412

Published: May 1, 2025

Language: Английский

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Introduction to AI in Agriculture

Published: Jan. 1, 2025

Language: Английский

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Application of Generative Artificial Intelligence in the aquacultural sector

Aquacultural Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 102568 - 102568

Published: May 1, 2025

Language: Английский

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A machine learning-based risk prediction model for diabetic oral ulceration

BMC Oral Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: May 22, 2025

Diabetic oral ulceration (DOU) is a prevalent and debilitating complication among diabetic patients, significantly impairing their quality of life imposing substantial economic burdens. Studies indicate that over 90% patients experience complications, with 45% suffering from ulcers. Clear diagnosis crucial for effective clinical management prognosis improvement. However, current diagnostic methods often fall short in early detection intervention. Machine learning (ML) has shown promise predicting disease development, yet no relevant predictive models DOU have been established. This study aimed to develop an ML-based model using examination, clinical, socioeconomic data. The dataset included 324 127 features. One-hundred-fold cross-validation was employed optimization feature selection. Data preprocessing involved handling missing values, scaling different range selection techniques such as Variance Threshold (VT), Mutual Information (MI), Inflation Factor (VIF). Four prediction models, Support Vector Classifier (SVC), Multi-layer Perceptron (MLP), Logistic Regression (LogReg), Perceptron, were established evaluated. SVC outperformed the other achieving accuracy (ACC) 0.95 area under ROC curve (AUC) 0.91. top five features contributing model's predictions number ulcers, diminished functional capacity, decayed or teeth, possession health insurance (commercial), Low-Density Lipoprotein (LDL-C), accounting 57.32% total importance. Oral examination indicators accounted 46.46%, serum lipid markers 6.93%, sociodemographic factors, personal lifestyles, cardiovascular diseases also played significant roles. demonstrated superior performance stability, making it suitable occurrence development patients. study's innovation lies comprehensive evaluation multiple including examinations, physiological indicators, self-management capabilities, facilitate efficient screening. findings highlight potential ML improving enabling timely interventions DOU, ultimately better patient outcomes. Future research should focus on validating across larger, multicenter cohorts further exploring long-term impact ML-guided management.

Language: Английский

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Assessment of Production Technologies on Dairy Farms in Terms of Animal Welfare

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6086 - 6086

Published: July 12, 2024

Dairy production on farms is based properly selected technologies implemented in various areas of the barn and outside livestock buildings. These are subject to assessment, for example, determine possibilities their further improvement given conditions farm. When assessing dairy technology a farm, human interests taken into account, including workload, time access modern tools supporting control processes. The aim this review identify discuss factors that may affect welfare cattle. considerations indicate cow feeding, watering housing, priority improve terms ensuring comfort animals using feed, water place rest. However, case assessment milking automation, key importance increasing was indicated, taking account cows, which an additional factor justifying implementation technical progress milking. excellent opportunity develop discussions cattle sustainable development priorities set improving production.

Language: Английский

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Classification of Behaviour in Conventional and Slow-Growing Strains of Broiler Chickens Using Tri-Axial Accelerometers

Animals, Journal Year: 2024, Volume and Issue: 14(13), P. 1957 - 1957

Published: July 2, 2024

Behavioural states such as walking, sitting and standing are important in indicating welfare, including lameness broiler chickens. However, manual behavioural observations of individuals often limited by time constraints small sample sizes. Three-dimensional accelerometers have the potential to collect information on animal behaviour. We applied a random forest algorithm process accelerometer data from Data three strains at range ages (from 25 49 days old) were used train test algorithm, unlike other studies, was further tested an unseen strain. When birds training strains, model classified behaviours with very good accuracy (92%) specificity (94%) sensitivity (88%) precision (88%). With new, strain, (94%), (91%), (96%) (91%). therefore successfully automatically detect across four different using accelerometers. These findings demonstrated that can be record supplement biomechanical research support reduction principle 3Rs.

Language: Английский

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