Application of udder surface temperature by infrared thermography for diagnosis of subclinical mastitis in Holstein cows located in tropical highlands

Early Detection of Sub-clinical Mastitis in Murrah Buffaloes through Udder Thermogram Analysis during the Natural Progression of Infection

Mastitis is a global production disease that needs an intelligent solution to tackle effectively. Infrared Thermography (IRT) is a non-invasive technology that could be incorporated into routine day-to-day farm activities to monitor the health status of the animals. In this study, the udder health status was routinely monitored for 30 days among 41 Murrah buffaloes via IRT and the California Mastitis Test (CMT). Further, somatic cell count (SCC), microbial identification, and milk quality parameters were also estimated for representative samples. The thermal imaging data obtained was tabulated and back propagated from the 0th day to the -10th day and front propagated from the 0th day to +10th day for all the udder quarters. Results revealed that on the 0th day, the mean of udder skin surface temperature (USST) and teat skin surface temperature (TSST) showed a difference (p < 0.05) in the sub-clinical mastitis (SCM) and clinical mastitis (CM) affected quarters to the healthy quarters, and their degree of difference was the highest. The indication of infection was signaled during the -9th to -5th day to the 0th day in SCM and CM cases. There was a steep increment in the temperature from -2nd and -1st day to the 0th day of infection. Sometimes, some quarters show an increment in temperature due to mastitis during morning hours but recover by evening milking due to the animal's innate immune system. Thus, the initiation period in which the udder gets assaulted is crucial in the early assessment of SCM by monitoring temperature change using IRT.

Correlation of udder thermogram and somatic cell counts as a tool for detection of subclinical mastitis in buffaloes

This study aimed to monitor the mammary health of 37 multiparous Murrah buffaloes through infrared thermography (IRT). Based on the California Mastitis Test (CMT) and milk somatic cell counts (SCC), buffaloes were grouped into healthy (H, n = 16), subclinical mastitis (SCM, n = 10), and clinical mastitis (CM, n = 11). Buffaloes were milked twice daily in the morning (5:00-6:00 AM) and evening (5:00-6:00 PM). Rectal temperature and respiratory rates were recorded, CMT was performed and thermal images of the mammary gland of all the buffaloes were taken before and after each milking. Milk samples were analysed after each milking for SCC, fat, Solids-Not-Fat (SNF), density, protein, lactose, salts, conductivity, and pH immediately in the laboratory from fresh milk samples. The surface temperature of the periocular region of both the eyes, muzzle, flank, and vagina were also taken. Thermal images were used to assess the surface temperature of the udder (USST), teat apex (TAT), teat barrel (TB1T), teat base (TB2T), and teat skin surface (TSST). Eye and USST showed significantly higher temperatures (p < 0.05), whereas skin surface temperatures (SST) of different body parts were non-significant in both SCM and CM animals than buffaloes in the H group. Milk SCC showed a positive correlation with conductivity (r > 0.7), salts, and pH (r < 0.6) and a negative correlation with fat, SNF, density, protein, and lactose. TAT, TB1T, TB2T, TSST, and USST were positively correlated with milk SCC. Receiver Operating Characteristic (ROC) analysis of H and SCM groups showed that USST before milking had optimum sensitivity (Se = 0.80) and specificity (Sp = 0.906) among the various skin temperatures recorded. Thermal images captured during the morning showed higher sensitivity compared to images taken in the evening. Results indicate IRT can be used to monitor the mammary health of buffaloes but using IRT in conjunction with milk SCC can help in the accurate prediction of SCM in dairy buffaloes.

Thermal Imaging and Dimensionality Reduction Techniques for Subclinical Mastitis Detection in Dairy Sheep

Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices.

Hypothalamic Neuromodulation and Control of the Dermal Surface Temperature of Livestock during Hyperthermia

Hyperthermia elicits several physiological and behavioral responses in livestock to restore thermal neutrality. Among these responses, vasodilation and sweating help to reduce core body temperature by increasing heat dissipation by radiation and evaporation. Thermoregulatory behaviors such as increasing standing time, reducing feed intake, shade-seeking, and limiting locomotor activity also increase heat loss. These mechanisms are elicited by the connection between peripheral thermoreceptors and cerebral centers, such as the preoptic area of the hypothalamus. Considering the importance of this thermoregulatory pathway, this review aims to discuss the hypothalamic control of hyperthermia in livestock, including the main physiological and behavioral changes that animals adopt to maintain their thermal stability.

Influence of repeated mastitis on the milk production and metabolic status in the subsequent lactation period of dairy cattle

Recurrent mastitis poses a common challenge on dairy farms. While the impact of repeated mastitis within the same lactation has been investigated, the difference from one lactation to the next, particularly concerning the change of milk and blood metabolites, remains unclear. This study aimed to examine the difference in milk yield, milk composition, and metabolic status in the subsequent lactation between healthy and repeated mastitis in the previous lactation. The study population comprised 50 cows chosen from 400 cows, with 25 having no history of mastitis and 25 experiencing mastitis more than three times during the last lactation. Following dry-off and calving, all cows initiated a new lactation, during which no mastitis was diagnosed until the sample collection period. In the group exposed to repeated mastitis, a significant decrease in milk fat levels was observed in the subsequent lactation, while no change was observed in milk somatic cell count (SCC). Milk collected from cows that had experienced repeated mastitis in the previous lactation exhibited significant increases in the levels of free amino acids, namely valine, proline, and alanine. However, no difference in plasma levels of these amino acids was noted. These results indicate that individuals exposed to repeated mastitis have persistent milk quality changes even after dry-off. Biomarker analysis suggested that the milk valine and proline showed a moderate biomarker potential on Kappa coefficients to characterize cows that have experienced repeated mastitis. Furthermore, the results of biomarker combinations for valine and proline provided the highest specificity (100 %), positive likelihood ratio (infinity), and substantial biomarker potential on kappa coefficients (0.68). These findings significantly enhance our understanding of the pathobiology and etiology of recurrent mastitis and provide a biomarker to characterize cows that have experienced repeated mastitis in the past.

Relationships of infrared thermography temperature with core temperature in goat

Rectal temperature is widely used as an indicator of animal health. However, rectal temperature is conventionally measured by an invasive method, which may reduce animal welfare. So, this study aimed to determine the relationships between the deep-body (core) temperature and body surface temperatures in goats and develop a linear regression equation to establish the core temperature based on body surface temperatures. Body surface temperatures (head, eye, muzzle, horn, back, scrotum and groin) of goats were measured by infrared thermography (IRT). Ambient temperatures were measured by digital thermometer. Core temperatures were measured by a digital vet thermometer. Pearson correlation analysis was used to analyze the relationship between body surface temperatures, ambient temperature, and core temperature. Simple linear regression analysis was used to develop core temperature assessment equations. Correlation analysis showed that groin temperature was highly correlated with core temperature, and low correlated with ambient temperature. The body surface temperature of other region was low correlated with core temperature, and highly correlated with ambient temperature. Regression analysis showed that the determination coefficient of core temperature assessment equation based on groin temperature was the highest (P < 0.0001, R2 = 0.55), and those based on surface temperature of other regions were low (P < 0.01, R2 ≤ 0.16). We concluded that body surface temperatures obtained by IRT could be used for the assessment of goat core temperature. The core temperature assessment equations developed by the temperature of the body surface, which is less affected by ambient temperature, was found to have a higher determination coefficient than the equations developed using body surface temperature that is more affected by ambient temperature.

Reliability of udder infrared thermography as a non-invasive technology for early detection of sub-clinical mastitis in Sahiwal (Bos indicus) cows under semi-intensive production system

The present study focused on Sahiwal cows, a prominent milch breed in tropical India, to correlate udder temperature with physiological markers of stress and inflammation during subclinical mastitis (SCM). The primary goal was to assess the potential of udder infrared thermography for the early detection of SCM under the semi-intensive production. Cows were categorized based on milk somatic cell counts (SCC), with healthy (H) cows having SCC <2 × 105 cells/mL and no history of mastitis, and cows with subclinical mastitis (SCM) and initial stages of clinical mastitis (CM) having quarter milk SCC of 2-5 × 105 and >5 × 105 cells/mL, respectively. Firstly, udder thermograms were analysed for udder skin surface temperature (USST), teat skin surface temperature (TSST), and teat apex temperature (TAT) using Fluke software to determine the optimal site for temperature measurement during intramammary infection. Secondly, milk samples were collected for automatic estimation of compositional changes, electrical conductivity, and pH. Thirdly, milk whey was separated for quantifying stress and inflammatory indicators, including cortisol, prolactin, and acute-phase proteins (APPs): milk amyloid A and milk haptoglobin using bovine-specific ELISA kits. Significant increases (p < 0.01) in USST, TSST, TAT, cortisol, and APPs were observed in SCM and CM compared to healthy cows, while prolactin levels decreased (p < 0.01). The correlation matrix revealed strong positive correlations of SCC with USST (r = 0.84, p < 0.01). In ROC analysis, USST demonstrated cut-off values of 37.74 and 39.58 °C, with accuracy (p < 0.05) of 98% for SCM and 95% for CM, surpassing both TAT and TSST. Therefore, the combination of these non-invasive methods increases the reliability and accuracy of infrared thermography for early detection of SCM, providing valuable insights for the development of a protocol for routine screening and udder health monitoring in indigenous dairy cows.

Seasonal assessment of mastitis using thermogram analysis in Sahiwal cows

"India is the world's leading producer of milk" and demands a non-invasive diagnostic tool like infrared thermography (IRT) to identify the costliest production disease, mastitis. It can form the basis of precision dairy farming. Therefore, the present study focuses on thermal imaging of the udder and teat quarters of Sahiwal cows during different seasons to identify subclinical (SCM) and clinical mastitis (CM) cases using the Darvi DTL007 camera. A total of 24-69 lactating Sahiwal cows were screened out using IRT regularly throughout the year. The intramammary infection status was further assessed using the CMT. The receiver operating characteristic analysis was carried out to develop the current study's cut-off for various thermographic parameters. The incidence for SCM and CM ranged from 26.47 to 38.75% and 17.83-22.79%, respectively during different seasons in Sahiwal udder quarters. The thermogram analysis revealed a significant difference (p < 0.01) in the mean values of the udder and teat surface temperature of Sahiwal cows between healthy, SCM, and CM during different seasons. The mean values of udder skin surface temperature (USST) during different seasons ranged between 29.07 and 36.91 °C, 31.51 to 37.88 °C and 32.42 to 38.79 °C among healthy, SCM, and CM-affected quarters, and correspondingly, the mean values of teat skin surface temperature (TSST) were 28.28 to 36.77 °C, 30.68 to 37.88 °C and 31.70 to 38.73 °C, respectively. Further results revealed an increase (p < 0.01) in the mean values of USST during winter, summer, rainy, and autumn as 2.44, 3.35; 0.97, 1.88; 1.06, 1.83; 1.29, 2.39 °C and TSST as 2.4, 3.42; 1.11, 1.96; 1.21, 2.19, 1.3, 2.4 °C of SCM, CM-affected quarters to healthy quarters, respectively, in Sahiwal cows. Thermograms showed a strong positive correlation with the CMT scores of SCM, CM cases, and healthy samples. Henceforth, irrespective of the seasons studied in the present work, IRT is an efficient, supportive tool for the early identification of subclinical mastitis.

Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis

Mastitis is one of the most widespread infectious diseases that adversely affects the profitability of the dairy industry worldwide. Accurate diagnosis and identification of pathogens early to cull infected animals and minimize the spread of infection in herds is critical for improving treatment effects and dairy farm welfare. The major pathogens causing mastitis and pathogenesis are assessed first. The most recent and advanced strategies for detecting mastitis, including genomics and proteomics approaches, are then evaluated . Finally, the advantages and disadvantages of each technique, potential research directions, and future perspectives are reported. This review provides a theoretical basis to help veterinarians select the most sensitive, specific, and cost-effective approach for detecting bovine mastitis early.

Udder Health Monitoring for Prevention of Bovine Mastitis and Improvement of Milk Quality

To maximize milk production, efficiency, and profits, modern dairy cows are genetically selected and bred to produce more and more milk and are fed copious quantities of high-energy feed to support ever-increasing milk volumes. As demands for increased milk yield and milking efficiency continue to rise to provide for the growing world population, more significant stress is placed on the dairy cow's productive capacity. In this climate, which is becoming increasingly hotter, millions of people depend on the capacity of cattle to respond to new environments and to cope with temperature shocks as well as additional stress factors such as solar radiation, animal crowding, insect pests, and poor ventilation, which are often associated with an increased risk of mastitis, resulting in lower milk quality and reduced production. This article reviews the impact of heat stress on milk production and quality and emphasizes the importance of udder health monitoring, with a focus on the use of emergent methods for monitoring udder health, such as infrared thermography, biosensors, and lab-on-chip devices, which may promote animal health and welfare, as well as the quality and safety of dairy products, without hindering the technological flow, while providing significant benefits to farmers, manufacturers, and consumers.

Research and application of a new multilevel fuzzy comprehensive evaluation method for cold stress in dairy cows

Effective and comprehensive evaluation of cold stress is critical for healthy dairy cow breeding in the winter. Previous studies on dairy cow cold stress have considered thermal environmental factors but not physiological factors or air quality. Therefore, this study aimed to propose a multilevel fuzzy comprehensive evaluation (FCE) method for cold stress in dairy cows based on the analytic hierarchy process (AHP) and a genetic algorithm (GA). First, the AHP was used to construct an evaluation index system for cold stress in dairy cows from 3 dimensions: thermal environment (temperature, relative humidity, wind speed, and illumination), physiological factors (respiratory rate, body surface temperature), and air quality [NH₃, CO₂, inhalable particulate matter (PM₁₀)]. Second, the consistency test of the judgment matrix was transformed into a nonlinear constrained optimization problem and solved using the GA. Next, based on fuzzy set theory, the comment set and membership function were established to classify the degree of cold stress into 5 levels: none, mild, moderate, high, and extreme. Then, the degree of cold stress in cows was obtained using multilevel fuzzy comprehensive judgment. To investigate the effect of illumination indicators on cold stress in dairy cows, 24 prelactation cows from the south and north sides were selected for a 117-d comprehensive cold stress evaluation. The results showed that the mean mild cold stress durations were 605.3 h (25.22 d) and 725.5 h (30.23 d) and the moderate cold stress durations were 67.2 h (2.8 d) and 96 h (4.0 d) on the south and north sides, respectively. Simultaneously, generalized linear mixed model showed that there were significant correlations between the daily cold stress duration and milk yield, feeding time, lying time, and active steps in the cows on both sides. This method can reasonably indicate cow cold stress conditions and better guide cold protection practices in actual production.

Thermography for disease detection in livestock: A scoping review

Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection research. In this review, we investigate parameters that are essential to the progression of this tool and make recommendations for their use based on the literature found and the veterinary thermography guidelines from the American Academy of Thermology. We analyzed a defined set of 109 articles concerned with the use of IRT in livestock related to disease and from these articles, parameters for accurate IRT were identified and sorted into the fields of camera-, animal- or environment-related categories to assess the practices of each article in reporting parameters. This review demonstrates the inconsistencies in practice across peer-reviewed articles and reveals that some important parameters are completely unreported while others are incorrectly captured and/or under-represented in the literature. Further to this, our review highlights the lack of measured emissivity values for live animals in multiple species. We present guidelines for the standards of parameters that should be used and reported in future experiments and discuss potential opportunities and challenges associated with using IRT for disease detection in livestock.

Delta thermal radiomics: An application in dairy cow teats

•

Infrared thermograms can indirectly detect blood flow and changes in blood flow on the skin.

•

Radiomics is a machine learning medical image analysis technique that reveals semantic and nonsemantic features.

•

Radiomics of dairy cow teat thermograms is a novel quantitative means of assessing changes in skin temperature before and after milking.

We describe a novel approach for analyzing thermal images by way of radiomics (i.e., thermal radiomics) and how it can be used to monitor short-term temperature changes of dairy cow hind teats; that is, delta thermal radiomics. The heat generated from metabolic activities and blood-flow patterns can be visualized using thermal radiography of the skin surface. The hind teats from 25 dairy cows were imaged with a digital thermal camera and the images were converted to medical images (DICOM format) by mapping the multi-channel colorized thermal image to a monochromatic image whose intensities represent temperature. The 50 teats (left and right hind) were then manually segmented by 2 investigators. Radiomics analysis, which is a common method of extracting semantic and nonsemantic image biomarkers from medical images for machine learning, was performed. To evaluate whether this approach can detect pre- and postmilking differences, 18 cows were imaged before and after milking, the teats were manually segmented, and radiomic calculations were performed. Student's t-test was used to provide an estimate of the likelihood of whether postmilking thermal image biomarkers are the same as premilking thermal image biomarkers, and Cohen's d was used to evaluate the size of the effect (d > 1.2). To evaluate uncertainties from manual segmentation, the Dice similarity score (DS) between the 2 investigators' segments was computed. The average DS (95% confidence limit) was 0.952 (0.913–0.982) when comparing the 2 investigators' segmentations. There was no significant difference in DS when comparing the left and right segmented teats, suggesting that teats can be segmented consistently. No differences (d < 0.36) were observed when comparing image biomarkers from one investigator's segments with the other's, suggesting that image biomarkers computed from one investigator's segmentation of teats are not likely to differ from those computed from the other investigator. When comparing image biomarkers before and after milking, 109 image biomarkers were analyzed, and 17 image biomarkers were simultaneously significant and exhibited effect size. Thus, delta thermal radiomics offers a noninvasive and quantitative method of monitoring skin temperature changes in humans and animals after an intervention. The advantage of this approach is that it can reveal both perceptible and imperceptible surface temperature features that may be useful for detecting and managing dairy teat health.