Using rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves

• Published in: Journal of dairy science Vol. 105; no. 5; pp. 4421 - 4433
• Main Authors: Teixeira, V.A., Lana, A.M.Q., Bresolin, T., Tomich, T.R., Souza, G.M., Furlong, J., Rodrigues, J.P.P., Coelho, S.G., Gonçalves, L.C., Silveira, J.A.G., Ferreira, L.D., Facury Filho, E.J., Campos, M.M., Dorea, J.R.R., Pereira, L.G.R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2022; Elsevier
• Subjects: Anaplasma marginale; Anaplasmosis - diagnosis; Anaplasmosis - microbiology; animal welfare; Animals; artificial intelligence; body temperature; body weight; bovine anaplasmosis; Cattle; Cattle Diseases - diagnosis; Cattle Diseases - microbiology; cattle production; dairy science; disease diagnosis; Erythrocytes; Female; heifers; hematocrit; machine learning; microscopy; neural networks; prediction; profitability; rumination; sensors; time series analysis; Vaccination - veterinary; sensors; Anaplasma marginale; machine learning; artificial intelligence

Bovine anaplasmosis causes considerable economic losses in dairy cattle production systems worldwide, ranging from $300 million to $900 million annually. It is commonly detected through rectal temperature, blood smear microscopy, and packed cell volume (PCV). Such methodologies are laborious, costly...