PhD in Veterinary Sciences for Animal Health and Food Safety

Contacts

Supervisor

Phd thesis

Towards interpretable and individualised Precision Dairy Farming: Machine Learning approaches to advance milk production modelling in Holstein Friesian cows

The ability to analyse and model future productivity dynamics in dairy cows is critical for effective herd management, resource allocation, and long-term sustainability in dairy farming. However, understanding and forecasting milk productivity remains a challenge due to the complex interactions between ge- netic, environmental, and managerial factors. This doctoral research advances precision dairy farming by leveraging Machine Learning (ML) techniques to develop interpretable and individualised tools for assessing milk productivity in Holstein Friesian cows within Automatic Milking Systems (AMSs).

Rather than predicting precise milk yields, this study introduces a novel framework for classifying cows into Productivity Groups (PGs) and investigat- ing the dynamics of milk production accross lactation periods. The research was conducted in two phases. In the first phase, a Multi-Clustering frame- work was developed to define Low and High PGs, integrating results from four distinct clustering algorithms using a novel merging index. The framework was validated across 16 farms, providing insights into herd-level productivity trends and identifying key factors influencing productivity continuity.

In the second phase, supervised ML models were employed to automate PG classification and explore the dynamics of future PGs. Additionally, a novel complexity metric was proposed to enhance the interpretability of Genetic Programming (GP) models. Feature importance analysis identified milking robot rate, milking frequency, and milk composition (fat, protein, and lactose percentages) as key predictors of productivity. Interpretable GP models also identified these features, further revealing how they relate with cows’ produc- tivity levels.

Lastly, this study explored the feasibility of predicting PGs up to two lac- tation periods ahead. Results indicated that the ability to model future pro- ductivity dynamics varied according to the lactation period being forecast, with the highest predictive stability observed for the second lactation and the lowest for the third, highlighting the challenges associated with productivity transitions.

The findings contribute to the broader adoption of ML in precision dairy farming by offering robust, interpretable, and actionable insights for herd man- agement.

Keywords: Automatic Milking System, Future Lactation Productivity Groups, Machine Learning, Multi-algorithm Clustering, Genetic Program- ming, Interpretability.

Research activities

Attended Congresses

• The 11th European Conference on Precision Livestock Farming (ECPLF 2024), 9th to 12th September, 2024, Bologna, Italy.
• EvoStar 2024 (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), 3rd to 5th April, 2024, Aberystwyth, Wales.
• GECCO 2023 (Genetic and Evolutionary Computation Conference), 15th to 19th July, 2023, Lisbon, Portugal. Link to the publication: https://dl.acm.org/doi/abs/10.1145/3583133.3590595
• EvoStar 2023 (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), 11th to 15th April, 2023, Brno, Czech Republic.
• WIVACE 2022 (XVI International Workshop on Artificial Life and Evolutionary Computation), 14th to 16th September, 2022, Gaeta, Italy. Link to the publication: https://link.springer.com/chapter/10.1007/978-3-031-31183-3_17
• 10th European Conference on Precision Livestock Farming (ECPLF 2022) and the 3rd International Conference on Precision Dairy Farming (PDC 2022), 29th August to 2nd September, Vienna, Austria.
• GECCO 2022 (The Genetic and Evolutionary Computation Conference), 9th to 13th July, 2022, Boston, United States. Link to the publication: https://dl.acm.org/doi/10.1145/3520304.3528806
• 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.

Courses

• SPECIES Summer School on Evolutionary Computation, by SPECIES Society. Moraira, Spain. 2023.
• Trasferimento di Conoscenze, UNITO 2023.
• Training course in Science Communication for PhD students, UNITO, 2023.
• Summer School on Automatic Algorithm Design, by CRIStAL laboratory on the University of Lille's Cité Scientifique campus. University of Lille. 2023.
• Science Comunication and Public Engagement, by Prof. Daniel Edward Chamberlain, Prof. Gianpiero Vigani, and Prof. Enrico Caprio. University of Torino. 2023.
• Big Data and Digital Tools Applied to Livestock Production, by Prof. Guilherme J. M. Rosa and Prof. João Dorea. University of Padova. 2022.
• Grant Writing: How to write a Competitive Application to a Funding Agency, by Prof. Roger Coulombe. University of Torino. 2022.
• Bayesian Statistics for Genetics, by Prof. Ken Rice and Prof. Jonathan Wakefield. University of Washington. 2022.
• Complex Networks: Theory, Methods, and Applications, coord. Prof. Carlo Piccardi. Lake Como School of Advanced Studies. 2022.
• Filmaking for Scientists, by Prof. Samer Angelone. University of Torino. 2021.

Training periods abroad

• Information Management School at NOVA University of Lisbon. From 6th of May 2024 to October 2024, under the supervision of Prof. Dr. Leonardo Vanneschi.
• Computer Science Department at Federal University of Minas Gerais, Brazil. From 29th of Spetember 2023 to 13th of March 2024, under the supervision of Prof. Dr. Gisele Pappa.
• Information Management School at NOVA University of Lisbon. From 20th of June 2023 to 2nd
  of August 2023, under the supervision of Prof. Dr. Leonardo Vanneschi.
• LASIGE Computer Science and Engineering Research Centre, Department of Informatics, Faculty of Sciences, University of Lisbon, Portugal. From 4th of October 2022 to 28th of February 2023, under the supervision of Prof. Dr. Sara Silva.
• Information Management School at NOVA University of Lisbon. From 4th of April 2022 to 4th of August 2022, under the supervision of Prof. Dr. Leonardo Vanneschi.

PhD Programme Credits

• Compulsory educational ECTS: 23.
• Total other theoretical ECTS: 92.5.
• Practical educational credits: 198.
• Total: 313.5.

List of Publications

• Rebuli, K. B., Ozella, L., Masía, F., Vrieze, E., Giacobini, M. (2025) Assessing the stability of herd productivity groups across lactation periods in automatic milking systems using multi-algorithm clustering. Comp and El in Agr, 235, 110295.
• Macedo, J., Gidey, H.K., Rebuli, K.B., Machado, P. (2024) Evolving User Interfaces: A Neuroevolution Approach for Natural Human-Machine Interaction. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) EvoMUSART 2024. LNCS, 14633. Springer, Cham.
• Ozella, L., Magliola, A., Vernengo, S., Ghigo, M., Bartoli, F., Grangetto, M., Forte, C., Montrucchio, G., Brotto Rebuli, K., Giacobini, M. (2024). A computer vision approach for the automatic detection of social interactions of dairy cows in automatic milking systems. Acta IMEKO, 13(2), Page range. https://doi.org/10.21014/acta-imeko.v11i2.1628
• Brotto Rebuli, K., Giacobini, M., Tallone, N., Vanneschi, L. (2023) Single and Multi-objective Genetic Programming Methods for Prediction Intervals. In: De Stefano, C., Fontanella, F., Vanneschi, L. (eds) Artificial Life and Evolutionary Computation. WIVACE 2022. Communications in Computer and Information Science, vol 1780. Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_17
• Rebuli, K. B. , Ozella, L. , Vanneschi, L., Giacobini, M. (2023) Multi- algorithm clustering analysis for characterizing cow productivity on au- tomatic milking systems over lactation periods, Computers and Elec- tronics in Agriculture, vol. 211, p. 108002
• Rebuli, K. B., Giacobini, M., Silva, S., Vanneschi. V., (2023) A Comparison of Structural Complexity Metrics for Explainable Genetic Programming. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO '23 Companion). Association for Computing Machinery, New York, NY, USA, 539–542.
• Ozella, L., Brotto Rebuli, K., Forte, C., Giacobini, M. (2023) A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems. Animals 13, no. 12: 1916. https://doi.org/10.3390/ani13121916
• Rebuli, K.B., Vanneschi, L. An Empirical Study of Progressive Insular Cooperative GP. SN COMPUT. SCI. 3, 119 (2022). https://doi.org/10.1007/s42979-021-00998-7
• Rebuli, K. B., Giacobini, M., Tallone, N., Vanneschi, L. (2022) A preliminary study of prediction interval methods with genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22). Association for Computing Machinery, New York, NY, USA, 530–533. https://doi.org/10.1145/3520304.3528806
• Brotto Rebuli, K., Vanneschi, L. (2022) An Empirical Study of Progressive Insular Cooperative GP. SN Comput. Sci. 3(2): 119
• Brotto Rebuli, K., Giacobini, M., Bertolotti, L. (2021) Caprine Arthritis Encephalitis Virus Disease Modelling Review. Animals (Basel). 11(5):1457. doi: 10.3390/ani11051457
• Brotto Rebuli, K., Vanneschi. L. (2021) Progressive Insular Cooperative GP. In Ting Hu and Nuno Lourenco and Eric Medvet editors, EuroGP 2021: Proceedings of the 24th EuroGP, 12691.

Publications