Integrating infrared thermography with cortisol for diagnosis of inflammatory diseases in dairy cows

Introduction

Animal diseases are commonly classified into production, endemic, and exotic categories, each carrying major implications for animal health and farm economics. Among the critical challenges in livestock farming, early detection of diseases is essential to mitigate health, welfare, and socio-economic losses. During recent years, thermal imaging has emerged as a promising methodology to strengthen early warning systems by enabling accurate discrimination between healthy and diseased animals, rapid data collection, and timely communication (McManus et al. 2022) [14] . Infrared thermography (IRT), a non-contact and non-invasive imaging tool, has been successfully used for farm animal health monitoring by various researchers (Luzi et [12,10,19] . Its diagnostic value lies in the detection of localized increases in surface temperature caused by vasodilation, enhanced blood flow, and metabolic activity. Such thermal changes often precede clinical symptoms, making IRT particularly useful for the early diagnosis of inflammatory conditions like laminitis, metritis, and mastitis. With current advances in automation and digital analysis, IRT is increasingly being integrated into routine herd health management. Compared to other imaging modalities, IRT is gaining prominence because it is simple to operate, remote-applicable, and farmer-friendly. Its potential for on-farm use in udder health monitoring has been highlighted as a cost-effective way to detect abnormalities early and reduce treatment expenses (Korelidou et al. 2024) [11] . Despite encouraging progress, it remains in a developmental stage, with research focused on overcoming inconsistencies observed under field conditions. Importantly, IRT findings have been corroborated by physiological biomarkers of inflammation. Increased plasma cortisol levels indicate hypothalamic-pituitary-adrenal (HPA) axis activation, reflecting systemic stress. In studies carried out in different livestock, cortisol levels have been found to be positively linked with variations in blood flow and surface temperature (Cai et [4,7] . The aim of this study, carried out in diseased and healthy cows, was to integrate thermal imaging with blood cortisol and evaluate its diagnostic potential as a comprehensive tool for monitoring both local and systemic disease processes.

Materials and Methods

Location of the study

The present research was carried out between January and June 2025 at the Livestock Research Centre (LRC), National Dairy Research Institute, Karnal, Haryana, India. This research location is situated within the Indo-Gangetic alluvial plains at an altitude of approximately 250 meters above mean sea level, located at 29°43′14.4″ N latitude and 76°58′55.2″ E longitude.

Experimental design

For experimentation, a total of 48 cows were selected from the institutional farm's lactating herd and experimental animals were grouped as 12 number of healthy cows and compared with 12 number of laminitis affected cows, 12 number of metritis affected cows, 12 number of mastitis affected cows. Randomization was performed while ensuring uniformity in parity (3-5) and lactation stage (60-90 days in milk). Blood samples for blood cortisol analyses, as well as thermal images from various thermal windows, were collected on the same day from all the cows. The cows were fed as per the standard feeding practices (ICAR, 2013) followed at the Livestock Research Centre (LRC) of ICAR-National Dairy Research Institute, Karnal. They were offered ad libitum green fodder: Maize (Zea mays), Jowar (Sorghum bicolor) and Hybrid Napier Grass (Pennisetum purpureum × Pennisetum glaucum). In addition to green fodder, the cows were supplemented with a concentrate mixture formulated to contain approximately 70% Total Digestible Nutrients and 20% Crude Protein. This highenergy, high-protein mixture was adjusted based on individual milk yield, following the formula of 1 kg of concentrate per 3 kg of milk produced, with a standard milk fat content of 4%. The concentrate mixture consisted of 33% maize, 21% ground nut cake, 12% mustered cake, 20% wheat bran, 11% de-oiled rice bran, 2% mineral mixture and 1% common salt. Ad libitum drinking water is accessed to all cows for 24 h.

Blood sampling and plasma separation

Approximately 10 ml of blood was collected aseptically from the jugular vein using sterile, heparinized vacutainer tubes (Vacuette®-Na-heparin, Greiner Bio-One GmbH, Austria), ensuring minimizing animal disturbance. Blood samples were centrifuged at 3000 rpm for 15 minutes at room temperature to separate plasma. Roughly 3-4 ml of plasma was obtained per sample and stored at-80 °C for further hormonal analysis.

Quantification of blood plasma cortisol

Plasma cortisol concentrations were determined using bovine-specific competitive ELISA kits (Bioassay Technology Laboratory, Shanghai, China). Optical density (OD) readings were obtained with a Tecan® Nano Quant ELISA reader (Infinite M200 PRO, Bio Screen Instruments Pvt. Ltd). The cortisol assay (Catalog No. EA0021Bo) had a sensitivity below 0.2 ng/ml and a detection range of 0.5-200 ng/ml. Both intra-and inter-assay coefficients of variation (CV) were under 12%. All reagents were prepared and the assay was performed according to the manufacturer's instructions. Final OD values were recorded at 450 nm using an ELISA plate reader (MultiSkan GO, Thermo Scientific).

Infrared imaging

The body surface temperatures of various areas were measured using a Fluke Tix 580 infrared thermal camera (Fluke Corp., Everett, WA, USA), capable of capturing 1280 × 960 resolution images and operating within a calibration range of-25.0 °C to 105.0 °C. Prior to data collection, the camera was calibrated, and its emissivity was set to 0.95. This device is equipped with a 240° rotating screen, a 640 × 480 display, a thermal sensitivity of 0.1 °C, and an imaging range of approximately 1-20 m. It utilizes an uncooled microbolometer with a 120 × 120 focal plane array for image scanning. Thermal images were obtained following the methods described by Choudhary et al. (2025) [7] and were subsequently analyzed using the Fluke® Systems Inc. Smart View Classic 4.4 software (https://assets.fluke.com/SmartView/TUpdates/Setup.exe). A high-contrast color scale was applied, and the "square marker" tool was manually used to measure the average temperatures (°C) of the cows' thermal windows. To maintain uniformity, the camera was positioned and captured exactly 1 meter from the animals. All thermal images were captured by the same operator to reduce variability associated with operator differences. Imaging was conducted in shaded areas to prevent direct sunlight interference and to limit environmental variation, with all photos taken on the same day as blood sampling. Wind effects were minimized by selecting sheltered spots, and the cows were given time to acclimate to the imaging environment before recording. Although no anti-reflective coatings were used, these standardized measures ensured uniform imaging conditions throughout the experiment.

Statistical Analysis

The statistical analysis of data was performed using IBM SPSS Statistics software, version 22.0 (SPSS Inc., Chicago, IL, USA). Within-group variations over time were evaluated using one-way ANOVA, and the Tukey HSD multiple comparison test was applied to identify significant differences. Results are presented as mean ± standard error (SE), and statistical significance was set at p<0.05. Associations between variables were assessed through Pearson's bivariate correlation, with two-tailed significance levels considered at p<0.05 and p<0.01. The magnitude and direction of correlations were interpreted according to the classification of Landis and Koch (1977), where correlation coefficients (r) were defined as: very weak (0 ≤ r<0.2), weak (0.2 ≤ r<0.4), moderate (0.4 ≤ r<0.6), strong (0.6 ≤ r<0.8), and very strong (0.8 ≤ r<1).

Results and Discussion

The present study carried out in diseased and healthy dairy cow's highlights the diagnostic potential of infrared thermography (IRT) in detecting region-specific surface temperature changes associated with laminitis, metritis, and mastitis, supported by haematological and endocrine profiles. The results obtained from this study demonstrated distinct thermal patterns depending on the underlying inflammatory condition, confirming that IRT can detect localized hyperthermia in tissues associated with inflammation and circulatory changes (Casas-Alvarado et al. 2020; Machado et al. 2021) [6,13] . Figure 2 and Figure 1.a illustrates the variations recorded in surface temperature across different thermal windows in cows affected by laminitis compared with healthy cows. The inflamed leg region showed the greatest increase in temperature, showing a significant (p<0.05) rise of 4.28 °C compared to the healthy cows. This was followed by the coronary band region, which exhibited a significant (p<0.05) increase in temperature of 3.72 °C. The forehead and left eye also demonstrated significant (p<0.05) increases of 2.18 °C and 2.03 °C, respectively. Other regions, including the muzzle (1.87 °C), right eye (1.67 °C), cheek (1.65 °C), ear (1.22 °C), and abdomen (1.16 °C), displayed relatively lower temperature elevations. Overall, limb-associated areas (leg region and coronary band region) showed the most pronounced thermal changes with laminitis-affected cows. In the laminitis-affected cows, the most pronounced temperature increases were observed in the inflammatory leg region and coronary band, followed by modest elevations in the forehead and ocular regions (Bobić et al. 2025) [3] . This is consistent with the inflammatory pathogenesis of laminitis, where vasodilation, increased peripheral blood flow, and inflammatory mediators release cause notable limb hyperthermia (Thompson et al. 2023) [25] . The heightened thermal signals in the coronary band region are of particular diagnostic interest as they could precede visible signs of hoof pathology, a finding corroborated by research indicating that early laminitis is detectable by identifying a temperature rise of >2 °C in hoof-associated regions (Alsaaod et al. 2014; Bobic et al. 2024) [1,2] . Figure 3 and Figure 1.b show the variations observed in surface temperature across different thermal windows in cows affected by metritis compared with healthy cows. A significant (p<0.05) increase in temperature was observed at the vulval tip with a rise of (2.26 °C), followed by the inner vulval region (1.94 °C) and the left eye (1.94 °C), respectively. The right eye exhibited a significant (p<0.05) rise of 1.63 °C, while the outer vulval area also showed a significant (p<0.05) increase of 1.47 °C. Moderate temperature elevations were recorded in the muzzle (0.89 °C), ear (0.80 °C), forehead (0.77 °C), and cheek (0.76 °C). The abdomen showed the smallest increase of 0.75 °C. Overall, the vulval regions and the eyes exhibited the most pronounced thermal deviations in metritis-affected cows compared with the healthy cows. In cows having metritis, the vulval tip and inner vulval region were the main hotspots, with ocular temperatures also significantly elevated. These findings align with reports that reproductive tract infections induce localized vascular and metabolic activity that is detectable by IRT in the perineal region (Sheldon et al. 2006; Talukder et al. 2015) [22,24] . The significant ocular temperature rise may reflect systemic inflammatory fever responses, as eye-region IRT has been validated as a reliable non-invasive indicator of core body temperature (Shu et al. 2022) [23] . Figure 4 and Figure 1.c depicts the variations observed in surface temperature across different thermal windows in cows affected by mastitis compared with healthy cows. The Teat Skin Surface Temperature (TSST) region showed significant (p<0.05) increase in temperature, rising by 3.58 °C compared with healthy cows, followed by the right Udder Skin Surface Temperature (USST) of 2.92 °C and Teat Apex Temperature (TAT) of 2.85 °C. Other regions exhibited a significant (p<0.05) increase in temperature, included the Teat Barrel Temperature (TBT) 2.27 °C, left USST (2.15 °C), and left eye temperature (2.08 °C). Moderate significant (p<0.05) increase in temperature were observed in the muzzle (1.94 °C), right eye (1.93 °C), ear temperature (1.55 °C), and forehead (1.41 °C). The cheek showed a smaller significant (p<0.05) rise of 1.19 °C, while the abdomen recorded the lowest temperature variation, at just of 0.52 °C. Overall, the udder associated regions particularly TSST, USST, TAT, TBT displayed the most pronounced temperature elevations in mastitis-affected cows, reflecting marked thermal responses linked to the mastitis condition. Blood cortisol levels increased significantly (p<0.05) in mastitis cows, reaching 9.21 ± 0.69 ng/ml, compared to 4.66 ± 0.52 ng/ml in laminitis cows, 3.71 ± 0.43 ng/ml in metritis cases, and only 1.43 ± 0.23 ng/ml in healthy animals (Figure 5).

Conclusion

The present study confirms that IRT effectively detects disease-specific thermal signatures in laminitis and mastitisaffected dairy cows, with pronounced temperature elevations in the leg, coronary band, and udder regions. However, IRT alone demonstrated limited diagnostic sensitivity for metritis, as internal uterine inflammation restricts surface thermal expression, and supportive diagnostic parameters are required for its confirmatory diagnosis. Blood cortisol levels were significantly elevated in diseased cows, particularly in mastitis cases, validating the thermal findings and reflecting systemic HPA axismediated stress responses. Overall, integration of IRT with complementary biochemical and clinical parameters offers a reliable, non-invasive, and comprehensive approach for early disease detection and routine herd health monitoring in dairy cattle.

References

1. Alsaaod M, Syring C, Dietrich J, Doherr MG, Gujan T, Steiner A. A field trial of infrared thermography as a non-invasive diagnostic tool for early detection of digital dermatitis in dairy cows. Vet J. 2014;199(2):281-285. doi:10.1016/j.tvjl.2013.11.028.
2. Bobic T, Mijic P, Gregic M, Gantner V. Evaluation of the hoof's temperature variations depending on lesion presence, measurement points and leg position. Vet Med. 2024;69(6):185-192. doi:10.17221/8/2024-VETMED.
3. Bobić T, Raguž N, Oroz M, Gregić M, Mijić P, Kranjac D, et al. Application of infrared thermography in the detection of hoof disease and lameness in cattle. Animals. 2025;15(8):1086-1098. doi:10.3390/ani15081086.
4. Cai Z, Cui J, Yuan H, Cheng M. Application and research progress of infrared thermography in temperature measurement of livestock and poultry animals: A review. Comput Electron Agric. 2023;205:107586-107586. doi:10.1016/j.compag.2022.107586.
5. Carvalho-Sombra TCF, Fernandes DD, Bezerra BMO, Nunes-Pinheiro DCS. Systemic inflammatory biomarkers and somatic cell count in dairy cows with subclinical mastitis. Vet Anim Sci. 2021;11:100165100165. doi:10.1016/j.vas.2021.100165.
6. Casas-Alvarado A, Mota-Rojas D, Hernández-Ávalos I, Mora-Medina P, Olmos-Hernández A, VerduzcoMendoza A, et al. Advances in infrared thermography: Surgical aspects, vascular changes, and pain monitoring in veterinary medicine. J Therm Biol. 2020;92:102664102664. doi:10.1016/j.jtherbio.2020.102664.
7. Choudhary M, Satheesan L, Kamboj A, Pal P, Kumar D, Singh P, et al. Effect of different heat mitigation strategies on the thermography of lactating water buffaloes during summer seasons under tropical conditions. Int J Biometeorol. 2025;69:1-12. doi:10.1007/s00484-025-03007-1.
8. Colak A, Polat B, Okumus Z, Kaya M, Yanmaz LE, Hayirli A. Early detection of mastitis using infrared thermography in dairy cows. J Dairy Sci. 2008;91(11):4244-4248. doi:10.3168/jds.2008-1258.
9. Gayathri SL, Bhakat M, Mohanty TK. Seasonal mastitis assessment in Holstein Friesian crossbred cows using infrared thermography. Ital J Anim Sci. 2024;23(1):1574-1591. doi:10.1080/1828051X.2024.2416065.
10. Kittur PM, Satheesan L, Madhusoodan AP, Sriranga KR, Kumar D, Kamboj A, Dang AK. Correlation of udder thermogram and somatic cell counts as a tool for detection of subclinical mastitis in buffaloes. Vet Res Commun. 2024;48(4):2721-2729. doi:10.1007/s11259-024-10384-2.
11. Korelidou V, Simitzis P, Massouras T, Gelasakis AI. Infrared thermography as a diagnostic tool for the assessment of mastitis in dairy ruminants. Animals. 2024;14(18):2691-2705. doi:10.3390/ani14182691.
12. Luzi F, Mitchell M, Nanni Costa L, Redaelli V. Thermography: current status and advances in livestock animals and in veterinary medicine. Brescia: Fondazione Iniziative Zooprofilattiche e Zootecniche; 2013. p. xv-216.
13. Machado NA, Da Costa LB, Barbosa-Filho JA, De Oliveira KP, De Sampaio LC, Peixoto MS, et al. Using infrared thermography to detect subclinical mastitis in dairy cows in compost barn systems. J Therm Biol. 2021;97:102881-102881. doi:10.1016/j.jtherbio.2021.102881.
14. McManus R, Boden LA, Weir W, Viora L, Barker R, Kim Y, et al. Thermography for disease detection in livestock: A scoping review. Front Vet Sci. 2022;9:965622-965622. doi:10.3389/fvets.2022.965622.
15. Mota-Rojas D, Pereira AM, Martínez-Burnes J, Domínguez-Oliva A, Mora-Medina P, Casas-Alvarado A, et al. Thermal imaging to assess the health status in wildlife animals under human care: Limitations and perspectives. Animals. 2022;12(24):3558-3568. doi:10.3390/ani12243558.
16. Sadek K, Saleh E, Ayoub M. Selective, reliable blood and milk biomarkers for diagnosing clinical and subclinical bovine mastitis. Trop Anim Health Prod. 2017;49(2):431-437. doi:10.1007/s11250-016-1190-7.
17. Saleh N, Allam TS, Omran A, Abdelfattah AM. Evaluation of changes in hemato-biochemical, inflammatory, and oxidative stress indices as reliable diagnostic biomarkers for subclinical mastitis in cows. Alex J Vet Sci. 2022;72(2):23-34. doi:10.5455/ajvs.140786.
18. Sarvesha K, Satyanarayana ML, Narayanaswamy HD, Rao S, Yathiraj S, Isloor S, et al. Haemato-biochemical profile and milk leukocyte count in subclinical and clinical mastitis affected crossbred cattle. J Exp Biol Agric Sci. 2017;5(1):1-6. doi:10.18006/2017.5(1).001.006.
19. Satheesan L, Kamboj A, Dang AK. Assessment of mammary stress in early lactating crossbred (Alpine × Beetal) does during heat stress in conjunction with milk somatic cells and non-invasive indicators. Small Rumin Res. 2024;240:107375-107375. doi:10.1016/j.smallrumres.2024.107375.
20. Satheesan L, Dang AK, Venkatesan RT, Kamboj A. Temporal dynamics and climatic factors on milk yield, milk quality and mammary stress biomarkers in tropical Sahiwal cows (Bos indicus) reared under semi-intensive production system. Theor Appl Climatol. 2025;156(7):370-382. doi:10.1007/s00704-025-05555-3.
21. Schaefer AL, Perez Marquez HJ, Bench CJ. The use of infrared thermography in the assessment of stress, health, and metabolism in domestic animals. Can J Anim Sci. 2025;105:1-13. doi:10.1139/cjas-2024-0067.
22. Sheldon IM, Lewis GS, LeBlanc S, Gilbert RO. Defining postpartum uterine disease in cattle. Theriogenology. 2006;65(8):1516-1530. doi:10.1016/j.theriogenology.2005.08.021.
23. Shu H, Li Y, Fang T, Xing M, Sun F, Chen X, et al. Evaluation of the best region for measuring eye temperature in dairy cows exposed to heat stress. Front Vet Sci. 2022;9:857777-857777. doi:10.3389/fvets.2022.857777.
24. Talukder S, Thomson PC, Kerrisk KL, Clark CEF, Celi P. Evaluation of infrared thermography body temperature and collar-mounted accelerometer and acoustic technology for predicting time of ovulation of cows in a pasture-based system. Theriogenology. 2015;83(4):739-748. doi:10.1016/j.theriogenology.2014.11.005.
25. Thomsen PT, Shearer JK, Houe H. Prevalence of lameness in dairy cows: A literature review. Vet J. 2023;295:105975-105975. doi:10.1016/j.tvjl.2023.105975.
26. Zhang F, Chen L, Tan X, Li Y, Duan Y. Research on sports injury recovery detection based on infrared thermography. In: Proc 7th Int Conf Deep Learn Technol. 2023:15-20. doi:10.1145/3613330.3613344.