56 research outputs found

    Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods

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    Rebuli, K. B., Ozella, L., Vanneschi, L., & Giacobini, M. (2023). Multi-Algorithm Clustering Analysis for Characterizing Cow Productivity on Automatic Milking Systems Over Lactation Periods. Computers And Electronics In Agriculture, 211(August 2023), [108002]. https://doi.org/10.2139/ssrn.4435365, https://doi.org/10.1016/j.compag.2023.108002---This study is supported by Compagnia di San Paolo (ROL 63369 SIME 2020.1713) and by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMSThis study proposes a novel approach for characterizing the milk productivity patterns of cows milked by Automatic Milking Systems (AMSs) within each lactation period, and for assessing its stability over time. AMSs enable real-time monitoring of udder health and milk quality during each milking episode, leading to an increasing amount of data that can be exploited to optimize herd management. Machine Learning (ML) algorithms are suitable for such situations, as they can handle multi-dimensional, heterogeneous, and large datasets. The methodology presented in this work used four clustering algorithms as unsupervised ML methods to cluster the cows within each lactation period. The clusters were grouped according to their productivity, and a merging index was defined to combine the clustering outcomes into a univocal result. The stability of the Productivity Groups (PGs) over time was analyzed. The proposed methodology was demonstrated using data from one farm with Holstein Friesians cows that exclusively uses the AMS. The PGs were found to be weakly stable over time, indicating that selecting cows for insemination based solely on their present or past lactation productivity may not be the most effective strategy. The study proposes using the same cows over all lactation periods to better understand the defining factors and dynamics of the PGs. Overall, the proposed framework provides a valuable tool for characterizing productivity groups and improving herd management practices in dairy farming.preprintepub_ahead_of_prin
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