Vol. 11, Special Issue 12 (2022)

Author(s):
Ronak Patel, Janaki Patel, Pragnesh Patel and Himadri Patel

Abstract:
Connectionism is an approach to the study of human cognition that utilizes mathematical models, known as connectionist networks or Artificial Neural Networks (ANNs). Lack of connectionist model applications in dairying is quite paradoxical as data analyses are usually carried out in the field and connectionist models have shown to be more powerful than classical statistical methods to perform such tasks. Rapid, low-cost alternatives that can provide approximate prediction of breeding values with acceptable accuracy could allow more timely selection and culling decisions. Rapid identification of superior males can lead to earlier collection and distribution of semen and more rapid genetic progress. The conventional regression procedures can-not evaluate the Multicollinearity between independent factors; hence, it may result in biased outcomes. ANN has been proposed in alleviating the limitations of the traditional regression methods, and can be used to handle non-linear and complex data, even when the data are imprecise and noisy. A large number of ANN-based learning algorithms have been reported. The major studies reporting the application of connectionist models to animal production and management include the prediction of cow performance in terms of predicting total milk, fat and protein production of individual cow for early identification of superior animals; life time milk yield prediction; animal identification; mobility & weight estimation; body condition scoring; detection of mastitis and its stage of progression; oestrus detection etc. The studies show that the result obtained from the conventional methods and connectionist models are highly correlated and with least differences.