Feasibility Study of  Dairy Cow Estrus detection based on milk using electronic nose system

Mortazavi, Seyed Nader; Mohtasebi, ُSeyed Saeid; Soltani, Mahmoud; Deghan, Mahdi; Mohtasebi, Seyed Morteza

doi:10.22034/jrmam.2023.13955.599

Feasibility Study of Dairy Cow Estrus detection based on milk using electronic nose system

Document Type : Research Paper

Authors

Seyed Nader Mortazavi ¹

ُSeyed Saeid Mohtasebi ²

mahmoud soltani ³

mahdi deghan ⁴

Seyed Morteza Mohtasebi ⁵

¹ Agricultural Engineering and Technology

² Department of Agricultural Machinery, University of Tehran

³ Charrah Daneshkadeh, Faculty Of Engineering And Technology

⁴ Department of Animal science

⁵ Department of Agricultural Machinery

https://doi.org/10.22034/jrmam.2023.13955.599

Abstract

Efficient detection of estrus is a permanent challenge for successful reproductive performance in dairy cattle. At present, in most parts of the world, estrus detection is done with observational basis, which has low productivity and causes great losses to this industry. Among the methods used to automatically detect estrus the electronic nose can be used to detect estrus from milk based on its odor due to advantages such as high speed, low operating cost and low manufacturing cost. In this study, an electronic nose system consisting of 7 metal oxide semiconductor (MOS) sensors was used to detect and predict the time of estrus. In this study, milk of 34 cows was sampled for 30 days in the Livestock of the University of Tehran in Karaj-Mohammadshahr and was tested on the same day, 26 of them showed signs of estrus and thus were examined for analysis. The system was analyzed along with pattern recognition methods such as Principal component analysis (PCA), Linear discriminant analysis (LDA), and Artificial neural network (ANN). At first, the feature vector was extracted from the response signal of the sensors and after sorting and classification, it was used as the input of pattern recognition methods. The results of pattern recognition methods were obtained as (94%) for principal component analysis, (86.33%) for Linear discriminant analysis, and (91.80%) for artificial neural network. The results showed well that the electronic nose system is a very efficient and fast way to classify and separate the different stages of the estrous cycle, particularly estrus stage.

Keywords

Estrus detection tool

machine olfaction

Gas sensor arrays

ANN

Subjects