Detection of Disease in Calves using Artificial Intelligence

被引:0
|
作者
Alzubi, Ahmad Ali [1 ]
机构
[1] King Saud Univ, Community Coll, Dept Comp Sci, Riyadh, Saudi Arabia
关键词
Animal health; Artificial intelligence; Bovine respiratory disease (BRD); Cattle farming; Deep learning; Machine learning; Neonatal calf diarrhoea (NCD); BOVINE RESPIRATORY-DISEASE; DAIRY-CATTLE; MASTITIS;
D O I
10.18805/IJAR.BF-1760
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Background: Livestock farming is experiencing a digital transformation and is becoming more information-driven. However, this type of data is often kept in separate storage towers, making it incapable of practicing its potential to boost animal welfare. Lumpy Skin Disease (LSD) is a serious threat to the health of cattle worldwide and has caused financial problems for many cattle farming enterprises. It has been shown that combining machine learning (ML) and artificial intelligence (AI) with biosensor data and conventional visual inspections can improve the identification and diagnosis of this serious condition. Methods: This study presents an extremely precise livestock farming framework that combines data streams from a wide array of disciplines of dairy cattle to see if ever wider/vast data sources enhance the overall projections for diseases and if using the more complicated prediction models can reimburse for less diverse data to some extent. Using images from the farming landscape, this study highlights the utility of convolutional neural networks (CNNs) in the identification of the Lumpy Skin Disease Virus (LSDV) in animals. Result: An analysis of the relative weight given to individual factors in predicting accuracy shows that disease in dairy cattle results from the intricate interactions between many life domains/parameters, such as housing, nutrition and climate; that prediction performance is enhanced by incorporating a wider range of data sources and that current information can be repurposed to produce useful information for vaccine development. The study highlights the potential of data-driven dairy interventions, focusing on artificial intelligence for disease prediction in cattle, to improve animal welfare and reduce the risk of disease. A Convolutional Neural Network (CNN) based model effectively classified skin conditions with an overall accuracy of 73.89% after 27 training epochs. This study demonstrates CNN's useful applications in the field of veterinary medicine by highlighting its potential for early detection of Lumpy Skin Disease (LSD).
引用
收藏
页码:2138 / 2145
页数:8
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