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Advancing mastitis assessment in dairy bovines via short milking tube thermography: A seasonal perspective.

S L GayathriMukesh BhakatTushar Kumar Mohanty
Published in: International journal of biometeorology (2024)
In India, where dairy production leads globally, infrared thermography (IRT) and short milking tube thermography specifically are vital for managing mastitis. Therefore, the present study focuses on thermal imaging of the udder and short milking tube (SMT) of the milking machine during the peak milking process of Sahiwal cows and Murrah buffaloes during winter, summer, rainy and autumn seasons to identify sub-clinical (SCM) and clinical mastitis (CM) cases using the Darvi DTL007 camera. The udder health was assessed using the California Mastitis Test, Somatic Cell Count (SCC) and IRT throughout the year. Log 10 SCC and thermogram analysis revealed a difference (p < 0.01) between healthy, SCM, and CM cases during different seasons in both breeds. Further results showed an increase (p < 0.01) in SMT thermograms of SCM and CM cases compared to healthy quarters in Sahiwal cows during winter, summer, rainy, and autumn were 4.26 and 7.51, 2.37 and 4.47, 2.20 and 3.64, 2.90 and 4.94 ºC, respectively and for Murrah buffaloes were 3.56 and 5.55, 2.70 and 3.81, 1.72 and 3.10, 3.14 and 4.42ºC, respectively. The highest degree of increase in milking udder skin surface temperature and SMT of SCM and CM cases compared to healthy quarters was observed during the winter and the least during the rainy season. Thus, regardless of the seasons examined in this study, SMT thermograms effectively assessed SCM and CM.
Keyphrases
  • public health
  • high resolution
  • mental health
  • gene expression
  • heat stress
  • machine learning
  • deep learning
  • peripheral blood
  • climate change
  • health information
  • convolutional neural network