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Current IssueEditorial BoardOnline First Articles
Indian Journal of
Animal Research
Print ISSN: 0367-6722
Online ISSN: 0976-0555
NASS Rating
6.40
SJR
0.233
CiteScore
0.606

Chief Editor:
M. R. Saseendranath
Kerala Veterinary and Animal Science University, Mannuthy, Thrissur, INDIA
Frequency:Monthly
Indexing:
Science Citation Index Expanded, BIOSIS Preview, ISI Citation Index, Biological Abstracts, Scopus, ...

Indian Journal of Animal Research, volume 49 issue 5 (october 2015) : 671-679

Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle

Nazira M. Mammadova, Ismail Keskin
1Department of Animal Science, Faculty of Agriculture, Selcuk University, 42075, Konya, Turkey.
Cite article:- Mammadova M. Nazira, Keskin Ismail (2025). Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle. Indian Journal of Animal Research. 49(5): 671-679. doi: 10.18805/ijar.5581.

ABSTRACT

Mastitis is an important problem, while I guess AI is a possible solution to detect subclinical mastitis in Holstein cows milked with automatic milking systems. Mastitis alerts were generated via ANN and ANFIS model with the input data of lactation rank (current lactation number), milk yield, electrical conductivity, average milking duration and season. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the sampling period. Cattle were judged healthy or infected based on somatic cell counts. This study undertook a detailed scrutiny of ANN, and ANFIS AI methodology; constructed and examined models for each; and chose optimal methods based on that examination. The two mastitis detection models were evaluated as to sensitivity, specificity and error rate. The ANN model yielded 80% sensitivity, 91% specificity, and 64% error and the ANFIS, 55%, 91% and 35%. These results suggest the ANN model is better predictor of subclinical mastitis than ANN based on Z-test (the hypothesis control for the difference between rates). AI models such as these are useful tools in the development of mastitis detection models. Prediction error rates can be decreased through the use of more informative parameters.

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