Background: Bovine ketosis is a major metabolic disorder emerging as a result of negative energy balance in the post calving period and subsequent lactation of lactating dairy animals. The measurement of the concentration of blood glucose was reported to be helpful for the early diagnosis of subclinical ketosis in buffaloes. Also, blood glucose will provide the overall energy status of the buffalo. 
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Methods: The experiment was conducted on 56 healthy lactating Murrah buffaloes. Blood samples were collected in two slots for two consecutive days in the morning hour in an anticoagulant-added sodium fluoride bulb and in clot activator plain tubes by jugular venipuncture. Also, glucose concentration was measured by a handheld device (&ldquo;Gluco One&rdquo;, Model BG-03) by puncturing the small branches of the ear vein and the results were recorded. The estimation of blood glucose plasma and serum was done by standard laboratory enzymatic method i.e. Glucose Oxidase- Peroxidase (GOD-POD) on an automated chemistry analyzer.

&nbsp;Result: There was no significant difference between the mean values of plasma glucose measured by the GOD-POD method and by the biosensor. But the regression analysis showed the blood glucose in plasma do not have a significant correlation with that measured by biosensor. The glucose measured in serum is significantly less than in plasma and measured by the biosensor. It is advisable to use fluoride-added preservative anticoagulant tubes and not to use a serum for the estimation of blood glucose. 
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Validation of Electrochemical Biosensor based Hand-held Device for the Measurement of Blood Glucose in Lactating Buffaloes (Bubalus bubalis)

Background: Lumpy skin disease (LSD) is a significant health concern for cattle globally and poses economic threats by affecting various aspects of cattle health. Integrating artificial intelligence (AI) and machine learning (ML) with visual inspections and biosensor data has shown promise in enhancing disease detection and diagnosis. The present study harnesses the potential of Convolutional Neural Networks (CNN) and image processing for detecting LSD.&nbsp; 
 
Methods: Using images from the agricultural landscape, this study highlights the significance of convolutional neural networks&nbsp; that identify the lumpy skin disease (LSD) in animals. Images are categorized into two groups: LSD (infected skin) and non-LSD (normal skin). This is achieved by applying a deeply designed deep learning model carefully built to fulfill this particular need. Evaluation metrics assess the model&rsquo;s performance, including accuracy, loss and a confusion matrix. 
 
Result: A CNN-based model trained for 50 epochs to classify skin conditions, achieved an 86.54% accuracy. The study underscores the potential of CNN in early LSD detection, paving the way for practical applications in veterinary medicine. Future work involves addressing dataset limitations, refining model parameters, reducing image noise, exploring different feature extraction methods and investigating additional animal skin conditions.

Lumpy Skin Disease Detection in Cattle by a Robust Approach using Advanced Convolutional Neural Networks

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. 
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Methods: This study&nbsp;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. 
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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&rsquo;s useful applications in the field of veterinary medicine by highlighting its potential for early detection of Lumpy Skin Disease (LSD).

Validation of Electrochemical Biosensor based Hand-held Device for the Measurement of Blood Glucose in Lactating Buffaloes (Bubalus bubalis)

Lumpy Skin Disease Detection in Cattle by a Robust Approach using Advanced Convolutional Neural Networks

Detection of Disease in Calves using Artificial Intelligence