Mastitis poses a significant economic challenge to the global dairy industry, causing approximately 35 billion USD in annual losses. Among its forms, subclinical mastitis (SM) is often overlooked due to the absence of obvious symptoms, yet it can lead to reduced milk production, increased treatment costs, and compromised dairy cow welfare. Traditional detection methods such as the California Mastitis Test (CMT) are reliable but rely on manual operation, making large-scale early screening difficult. How can non-invasive technology be used to accurately identify subclinical mastitis while considering the impact of environmental factors on detection results?
Dr. Weerasinghe Pathirage Chamila Gayani WEERASINGHE and colleagues from the University of Peradeniya in Sri Lanka have proposed a new approach by determining regional temperature thresholds using infrared thermography (IRT), offering a novel solution for efficient detection. The related research has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025638).
The research team selected 658 small and medium-scale dairy farms across four typical agricultural regions in Sri Lanka—Up Country (UP), Mid Country (MC), Coconut Triangle (CT), and Western Province (WP)—and conducted IRT examinations on 4274 udder quarters of 1074 lactating cows. Results indicated that the udder skin surface temperature (USST) of SM-positive quarters was significantly higher than that of healthy quarters, with temperature differences (ΔT) varying by region: 2.49 ℃ in UP, 2.17 ℃ in MC, 1.90 ℃ in WP, and 1.86 ℃ in CT. This finding demonstrates that IRT can screen for SM by measuring the temperature difference between the udder surface and flank skin; a ΔT exceeding the regional threshold indicates a suspected infection.
Environmental temperature and the temperature-humidity index (THI) exerted a significant influence on these thresholds. Data showed that UP, with the lowest environmental temperature (22.6 ℃), had the highest threshold, while WP, with the highest temperature (29.7 ℃), had the lowest. Regression analysis confirmed that for each 1 ℃ increase in environmental temperature, the ΔT threshold decreased by 0.08 ℃; for each 1-unit increase in THI, the threshold decreased by 0.05 ℃. In high-temperature environments, dairy cows activate thermoregulatory mechanisms, leading to synchronous increases in both USST and flank skin temperature, which reduces the temperature difference. Thus, judgment criteria must be adjusted based on regional climatic conditions.
This study integrates IRT with regional environmental parameters to establish an SM detection model suitable for tropical regions. Compared to traditional methods, IRT offers non-contact, rapid, and batch-detection advantages. Farmers can initially assess infection status by capturing udder images with handheld thermal cameras and comparing the resulting temperature differences against regional thresholds.
While potential errors related to shooting distance and angle exist, they can be effectively controlled through standardized operations. Future research is recommended to integrate this technology into herd management software and combine it with indicators such as somatic cell count to enhance detection accuracy. This achievement provides a practical tool for disease prevention and control in the dairy industry of developing countries, promising to promote precision management in small and medium-scale farms, reduce economic losses, and improve animal welfare.
DOI：10.15302/J-FASE-2025638