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Karina Brotto Rebuli

  • Phd: 37th cycle
  • Matriculation number: 975638
  • ORCID: orcid.org/0000-0002-8606-0754

Phd thesis

Development of Machine Learning Models Applied on Automatic Milking System Data

This PhD project is part of the Circular Health for Industry (CH4I) project, whose general goals are (i) to study how to collect, manage and analyse data in a Circular Health approach without keeping separate data silos about human health, animals and plants and (ii) to develop Artificial Intelligence algorithms that work on them, to improve the infrastructure for collecting and analysing such data and to reskill workers for the adoption of AI technologies (https://ch4i.di.unito.it/). Within the CH4I, the work in being developed as part of the Subproject 2 Agrifood, specifically in the context of milk production of a herd that uses an Automatic Milking System (AMS) exclusively from 2016.
AMS, also called milking robots, generate a plenty of data on milk production and quality, milking behaviour and udder health, providing detailed information about each cow. This was not easily obtained with previous conventional systems and the extensive collection of data through the AMS has led to an exponentially growing amount of data [1]. However, very little has been done with this data. In literature, we could find 55 journal papers that analyses AMS data, and just 5 of them focused on milk production [2-6]. The goal of this PhD project is to develop methodologies and algorithms to fill this gap, particularly on milk production modelling with Machine Learning (ML) methods.
Currently, there are two branches of work in which the project is being developed:

(i) A clustering analysis with the milking data with the scope of characterising over lactation periods the groups of cows by their productivity. Preliminar results will be presented at the International Conference on Precision Dairy Farming, Vienna 2022.

(ii) The development of prediction interval Genetic Programming models to be applied on modelling milk production data. Preliminary results will be presented at the Genetic and Evolutionary Computation Conference (GECCO), Boston 2022.

Besides the aforementioned work, a third analysis will be applied to the Milk Production Data data with Automatic Machine Learning (AutoML) modelling. AutoML systems automatically set the algorithm and the hyperparameters of supervised learning optimisation problems. This automation intends not only improve the model’s prediction, but also to spread the use of ML techniques. In the current data analysis pipeline, it is difficult to devise and deploy ML solutions as the whole exercise begins with a lengthy data provisioning process, continues with finding the right collaborators, and involves a continuous back-and-forth between ML experts and domain experts [8].

References:

[1] Jacobs, J. A., J. M. Siegford (2012). Invited review: The impact of automatic milking systems on dairy cow management, behavior, health, and welfare. Journal of Dairy Science, 95(5), 2227-2247.
[2] Jensen, D. B., van der Voort, M., Hogeveen, H (2018). Dynamic forecasting of individual cow milk yield in automatic milking systems. Journal of Dairy Science, 101(11), 10428-10439.
[3] Fuentes, S., Viejo, C. G., Cullen, B. , Tongson, E., Chauhan, S., S., Dunshea, F. R.(2020) Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters. Sensors 2020, 20(10), 2975.
[4] Piwczynski, D., SitkowskaB., Aerts, J., Schork, P. M. 2020. Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees. Animal Science Journal 2020, 91.
[5] Klis, P., Piwczynski, D., Sawa, A., Sitkowska, B. (2021). Prediction of Lactational Milk Yield of Cows Based on Data Recorded by AMS during the Periparturient Period. Animals 2021, 11, 383.
[6] Bovo, M., Agrusti, M., Benni, S., Torreggiani, D., Tassinari, P. (2021). Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions. Animals 2021, 11, 1305.
[7] Waring, J., Lindvall, C., Umeton, R. (2020). Automated machine learning - Review of the state-of-the-art and opportunities for healthcare Artificial Intelligence In Medicine 104. 101822.

Research activities

Attended Congresses

GECCO 2022 (The Genetic and Evolutionary Computation Conference), 9th to 13th July, 2022, Boston, United States.

EvoStar 2022 (European Conference on Genetic Programming, International Conference on the Applications of Evolutionary Computation, European Conference on Evolutionary Computation in Combinatorial Optimization and International Conference on Computational Intelligence in Music, Sound, Art and Design), 20th to 22th April, 2022, Madrid, Spain.

Game of Research - North Edition. 16h December, 2021. Turin, Italy.

 

Last update: 28/07/2022 16:25

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