AI-based prediction and detection of early-onset of digital dermatitis in dairy cows using infrared thermography.

Digital dermatitis (DD) is a common foot disease that can cause lameness, decreased milk production and fertility decline in cows. The prediction and early detection of DD can positively impact animal welfare and profitability of the dairy industry. This study applies deep learning-based computer vision techniques for early onset detection and prediction of DD using infrared thermography (IRT) data.

Machine learning approaches to predict and detect early-onset of digital dermatitis in dairy cows using sensor data.

The present study aimed to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD) and (2) DD prediction in dairy cows. Our machine learning model, which was based on the Tree-Based Pipeline Optimization Tool (TPOT) automatic machine learning method, for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79% on the test set, while the model for the prediction of DD 2 days prior to the appearance of the first clinical signs, which was a combination of K-means and TPOT, has reached an accuracy of 64%. The proposed machine learning models have the potential to help achieve a real-time automated tool for monitoring and diagnosing DD in lactating dairy cows based on sensor data in conventional dairy barn environments.

A Fixed Cohort Field Study of Gene Expression in Circulating Leukocytes From Dairy Cows With and Without Mastitis.

Specifically designed gene expression studies can be used to prioritize candidate genes and identify novel biomarkers affecting resilience against mastitis and other diseases in dairy cattle. The primary goal of this study was to assess whether specific peripheral leukocyte genes expressed differentially in a previous study of dairy cattle with postpartum disease, also would be expressed differentially in peripheral leukocytes from a diverse set of different dairy cattle with moderate to severe clinical mastitis. Four genes were selected for this study due to their differential expression in a previous transcriptomic analysis of circulating leukocytes from dairy cows with and without evidence of early postpartum disease.

Physiological responses of Holstein calves to hot weather conditions.

The objectives of the study described were to (1) compare environmental temperature-humidity index (THI) with the THI measured within two different calf housing systems and (2) determine how THI affects Holstein heifer calf body temperatures, serum cortisol concentrations, and serum thyroxine concentrations. At 24 to 48 h of age, calves were assigned to one of two individual housing treatments: (1) stalls in a three-sided barn (n = 8) or 2) hutches placed outside (n = 8). Calves were observed until 42 days of age during the summer months.

Economic and livestock health impacts of birds on dairies: Evidence from a survey of Washington dairy operators.

The survey described in this research paper aimed to investigate the economic and health impacts of birds on dairies. Birds are common pests on dairies, consuming and contaminating feed intended for cattle. As a result, dairy operators experience increased feed costs and increased pathogen and disease risk.