AIMC Topic: Eating

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Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms.

BMC genomics Mar 31, 2025
BACKGROUND: Feed efficiency (FE) is an essential trait in livestock species because of the constant demand to increase the productivity and sustainability of livestock production systems. A better understanding of the biological mechanisms associated...
Algorithms Female Animals Machine Learning Eating Animal Feed Sheep Genomics Epigenomics DNA Methylation
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Providing concentrate feed outside of the milking robot increases feed intake in dairy cows without reducing motivation to visit the robot.

Animal : an international journal of animal bioscience Feb 10, 2025
Appropriate and adequate feeding is essential to maintaining good health, productivity and welfare of dairy cows. Within automatic milking systems, concentrate feed is offered inside the milking robot, and is thought to play a key role in motivating ...
Feeding Behavior Dairying Robotics Female Eating Lactation Animal Feed Animals Milk Cattle Motivation
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Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks.

Tropical animal health and production Jan 30, 2025
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to w...
Neural Networks, Computer Goats Eating Body Weight Animals Male Animal Husbandry Animal Feed Animal Nutritional Physiological Phenomena
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Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.

Journal of medical Internet research Nov 28, 2024
BACKGROUND: Accurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are...
Nutrients Male Nutrition Assessment Deep Learning Artificial Intelligence Adolescent Female Humans Child Adult Eating
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Performance of expert fuzzy systems for prediction of rabbit feed intake after weaning.

Tropical animal health and production Nov 9, 2024
The postweaning phase is stressful for rabbits due to maternal separation and the introduction of solid feed. Stress can be aggravated when animals are subjected to thermal discomfort. Therefore, thermal variables, such as air temperature, which infl...
Animal Husbandry Fuzzy Logic Rabbits Eating Animal Feed Weaning Female Temperature Animals Male
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A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.

JMIR formative research Nov 5, 2024
BACKGROUND: Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous pat...
Eating Food Service, Hospital Humans Artificial Intelligence Energy Intake
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Prediction of growth and feed efficiency in mink using machine learning algorithms.

Animal : an international journal of animal bioscience Sep 16, 2024
The feed efficiency (FE) expresses as the amount of feed required per unit of BW gain. Since feed cost is the major input cost in the mink industry, evaluating of FE is a crucial step for competitiveness of the mink industry. However, the FE measures...
Female Animals Male Animal Husbandry Eating Animal Feed Machine Learning Weight Gain Algorithms Mink
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Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep Learning.

Sensors (Basel, Switzerland) Mar 22, 2024
In this paper, we propose an amount estimation method for food intake based on both color and depth images. Two pairs of color and depth images are captured pre- and post-meals. The pre- and post-meal color images are employed to detect food types an...
Food Eating Postprandial Period Computer Simulation Deep Learning
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Association between household income levels and nutritional intake of allergic children under 6 years of age in Korea: 2019 Korea National Health and Nutrition Examination Survey and application of machine learning.

Frontiers in public health Jan 8, 2024
INTRODUCTION: This study investigated the prevalence of allergic diseases in Korean children aged 6 and below, focusing on the interplay between nutritional status, household income levels, and allergic disease occurrence.
Child Dietary Fiber Eating Female Nutrition Surveys Humans Asthma Potassium Rhinitis, Allergic Iron Republic of Korea Child, Preschool Thiamine
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Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.

Sensors (Basel, Switzerland) Aug 26, 2022
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer's disease, or other...
Humans Artificial Intelligence Food Adolescent Nutrition Assessment Eating Feeding Behavior
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