Prediction of first lactation 305-day milk yield based on weekly test day records using artificial neural networks in Sahiwal Cattle

B. DongreV., R. S. Gandhi, A. P. Ruhil, K. GuptaR., K. SinghR.
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引用次数: 9

Abstract

In the present study, first lactation 305-day lactation milk yield (FL305DMY) was predicted by artificial neural network (ANN) using monthly test day milk yields records of 588 Sahiwal cows. A total of five monthly test day milk yields (2, 3, 5, 7, 8 monthly test day record) were used in neural networks to train data using Bayesian regularization (BR) algorithm. Results showed that the accuracy of prediction of all the models increased with the addition of test day milk yields as input variables. The best neural network model was able to predict FL305DMY with 93.18% accuracy. Further, comparison was made between multiple linear regression (MLR) and ANN for accuracy of prediction and there was no significant different found between ANN and MLR for prediction of FL305DMY in Sahiwal cows.
基于周试验日记录的人工神经网络预测萨希瓦尔牛首次泌乳305天产奶量
本研究利用588头Sahiwal奶牛每月试验日产奶量记录,采用人工神经网络(ANN)预测首次泌乳305 d泌乳产奶量(FL305DMY)。利用5个月试验日产奶量(2、3、5、7、8个月试验日记录)在神经网络中使用贝叶斯正则化(BR)算法训练数据。结果表明,加入试验日产奶量作为输入变量,各模型的预测精度均有所提高。最佳神经网络模型预测FL305DMY的准确率为93.18%。进一步比较了多元线性回归(MLR)与人工神经网络(ANN)预测FL305DMY的准确率,发现人工神经网络与MLR预测Sahiwal奶牛FL305DMY的准确率无显著差异。
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