A Model for the Prediction of Lifetime Profit Estimate of Dairy Cattle (Student Abstract)


  • Vahid Naghashi Université du Québec à Montréal
  • Abdoulaye Banire Diallo Université du Québec à Montréal




Dairy Cattle, LSTM, Multivariate Time Series, Forcasting, Profit Prediction


In livestock management, the decision of animal replacement requires an estimation of the lifetime profit of the animal based on multiple factors and operational conditions. In Dairy farms, this can be associated with the profit corresponding to milk production, health condition and herd management costs, which in turn may be a function of other factors including genetics and weather conditions. Estimating the profit of a cow can be expressed as a spatio-temporal problem where knowing the first batch of production (early-profit) can allow to predict the future batch of productions (late-profit). This problem can be addressed either by a univariate or multivariate time series forecasting. Several approaches have been designed for time series forecasting including Auto-Regressive approaches, Recurrent Neural Network including Long Short Term Memory (LSTM) method and a very deep stack of fully-connected layers. In this paper, we proposed a LSTM based approach coupled with attention and linear layers to better capture the dairy features. We compare the model, with three other architectures including NBEATs, ARIMA, MUMU-RNN using dairy production of 292181 dairy cows. The results highlight the performence of the proposed model of the compared architectures. They also show that a univariate NBEATs could perform better than the multi-variate approach there are compared to. We also highlight that such architecture could allow to predict late-profit with an error less than 3$ per month, opening the way of better resource management in the dairy industry.




How to Cite

Naghashi, V., & Diallo, A. B. (2022). A Model for the Prediction of Lifetime Profit Estimate of Dairy Cattle (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13021-13022. https://doi.org/10.1609/aaai.v36i11.21647