KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/35140| Title: | Prediction of first lactation 305-day milk yield in Karan-Fries dairy cattle using ANN modelling |
| Other Titles: | Not Available |
| Authors: | A.K. Sharma R.K. Sharma H.S. Kasana |
| ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
| Author's Affiliated institute: | ICAR::National Dairy Research Institute Thapar Institute of Engineering & Technology, Patiala-147004, Punjab, India. |
| Published/ Complete Date: | 2006-12-04 |
| Project Code: | Not Available |
| Keywords: | Artificial neural networks Back-propagation Dairy Generalization Karan Fries cows Milk-yield prediction Regularization Weight decay |
| Publisher: | Elsevier B.V. |
| Citation: | Sharma, A.K., Sharma, R.K. and Kasana, H.S., 2007. Prediction of first lactation 305-day milk yield in Karan-Fries dairy cattle using ANN modelling. Applied Soft Computing 7(3): 1112–1120. doi:10.1016/j.asoc.2006.07.002. |
| Series/Report no.: | Not Available; |
| Abstract/Description: | In this paper, an artificial neural network (ANN) model is proposed to predict the first lactation 305-day milk yield (FLMY305) using partial lactation records pertaining to the Karan Fries (KF) crossbred dairy cattle. A scientifically determined optimum dataset of representative breeding traits of the cattle is used to develop the model. Several training algorithms, viz., (i) gradient descent algorithm with adaptive learning rate; (ii) Fletcher–Reeves conjugate gradient algorithm; (iii) Polak–Ribiére conjugate gradient algorithm; (iv) Powell–Beale conjugate gradient algorithm; (v) Quasi-Newton algorithm with Broyden, Fletcher, Goldfarb, and Shanno (BFGS) update; and (vi) Levenberg–Marquardt algorithm with Bayesian regularization; along with various network architectural parameters, i.e., data partitioning strategy, initial synaptic weights, number of hidden layers, number of neurons in each hidden layer, activation functions, regularization factor, etc., are experimentally investigated to arrive at the best model for predicting the FLMY305. Also, a multiple linear regression (MLR) model is developed for the milk-yield prediction. The performances of ANN and MLR models are compared to assess the relative prediction capability of the former model. It emerges from this study that the performance of ANN model seems to be slightly superior to that of the conventional regression model. Hence, it is recommended that the ANNs can potentially be used as an alternative technique to predict FLMY305 in the KF cattle. |
| Description: | Research findings of the first author's PhD work. |
| ISSN: | Not Available |
| Type(s) of content: | Research Paper |
| Sponsors: | Not Available |
| Language: | English |
| Name of Journal: | Applied Soft Computing |
| Volume No.: | 7(3) |
| Page Number: | 1112-1120 |
| Name of the Division/Regional Station: | Dairy Economics, Statistics and Management Division ICAR-NDRI Karnal; and School of Mathematics and Computer Applications, TIET Patiala. |
| Source, DOI or any other URL: | https://doi.org/10.1016/j.asoc.2006.07.002 |
| URI: | http://krishi.icar.gov.in/jspui/handle/123456789/35140 |
| Appears in Collections: | AS-NDRI-Publication |
Files in This Item:
There are no files associated with this item.
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.