Towards an Effective Decision-making System based on Cow Profitability using Deep Learning

Charlotte Frasco, Charlotte Frasco, Maxime Radmacher, René Lacroix, Roger Cue, Roger Cue, Petko Valtchev, Claude Robert, Mounir Boukadoum, Marc-André Sirard, Abdoulaye Diallo

2020

Abstract

Life-time profitability is a leading factor in the decision to keep a cow in a herd, or sell it, that a dairy farmers face regularly. A cow’s profit is a function of the quantity and quality of its milk production, health and herd management costs, which in turn may depend on factors as diverse as animal genetics and weather. Improving the decision making process, e.g. by providing guidance and recommendation to farmers, would therefore require predictive models capable of estimating profitability. However, existing statistical models cover only partially the set of relevant variables while merely targeting milk yield. We propose a methodology for the design of extensive predictive models reflecting a wider range of factors, whose core is a Long Short-Term Memory neural network. Our models use the time series of individual features corresponding to earlier stages of cow’s life to estimate target values at following stages. The training data for our current model was drawn from a dataset captured and preprocessed for about a million cows from more than 6000 different herds. At validation time, the model predicted monthly profit values for the fifth year of each cow (from data about the first four years) with a root mean squared error of 8.36 $/cow/month, thus outperforming the ARIMA statistical model by 68% (14.04 $/cow/month). Our methodology allows for extending the models with attention and initializing mechanisms exploiting precise information about cows, e.g. genomics, global herd influence, and meteorological effects on farm location.

Paper Citation

in Harvard Style

Frasco C., Radmacher M., Lacroix R., Cue R., Valtchev P., Robert C., Boukadoum M., Sirard M. and Diallo A. (2020). Towards an Effective Decision-making System based on Cow Profitability using Deep Learning. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 949-958. DOI: 10.5220/0009174809490958

in Bibtex Style

@conference{icaart20,
author={Charlotte Frasco and Maxime Radmacher and René Lacroix and Roger Cue and Petko Valtchev and Claude Robert and Mounir Boukadoum and Marc-André Sirard and Abdoulaye Diallo},
title={Towards an Effective Decision-making System based on Cow Profitability using Deep Learning},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={949-958},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009174809490958},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Towards an Effective Decision-making System based on Cow Profitability using Deep Learning
SN - 978-989-758-395-7
AU - Frasco C.
AU - Radmacher M.
AU - Lacroix R.
AU - Cue R.
AU - Valtchev P.
AU - Robert C.
AU - Boukadoum M.
AU - Sirard M.
AU - Diallo A.
PY - 2020
SP - 949
EP - 958
DO - 10.5220/0009174809490958