Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis
| Autor: | Romain Lardy, Isabelle Veissier, Violaine Antoine, Mathieu Silberberg, Nicolas Wagner, Marie-Madeleine Mialon, Jonas Koko |
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| Přispěvatelé: | Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Projet ANR : CAP 20-25 - 16-IDEX-0001 - CAP 20-25 (I-Site (2017), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25)INRAE (Phase division) Universite Clermont Auvergne (SPI doctorate school), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), ANR-16-IDEX-0001,CAP 20-25,CAP 20-25(2016) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
bovin [SDV]Life Sciences [q-bio] education bien-être animal Horticulture Biology Machine learning computer.software_genre 01 natural sciences comportement animal animal welfare Bolus (medicine) Animal activity Group level animal health business.industry santé animale Continuous monitoring santé animal food and beverages Forestry 04 agricultural and veterinary sciences rythme d'activité Regression Computer Science Applications Subacute ruminal acidosis Multilayer perceptron 040103 agronomy & agriculture Monitor ph 0401 agriculture forestry and fisheries Artificial intelligence MACHINE LEARNING business Agronomy and Crop Science computer 010606 plant biology & botany |
| Zdroj: | Computers and Electronics in Agriculture Computers and Electronics in Agriculture, Elsevier, 2020, 170, ⟨10.1016/j.compag.2020.105233⟩ Computers and Electronics in Agriculture, 2020, 170, ⟨10.1016/j.compag.2020.105233⟩ Computers and Electronics in Agriculture (170), . (2020) |
| ISSN: | 0168-1699 |
| DOI: | 10.1016/j.compag.2020.105233⟩ |
| Popis: | Sickness behaviour is characterised by a lethargic state during which the animal reduces its activity, sleeps more and at times when normally awake, reduces its feed and water intake, and interacts less with its environment. Subtle modifications in behaviour can materialise just before clinical signs of a disease. Recent sensor developments enable continuous monitoring of animal behaviour, but the shift to abnormal animal activity remains difficult to detect. We explored the use of Machine Learning (ML) to detect abnormal behaviour from continuous monitoring. We submitted 14 cows (Bos taurus) to Sub-Acute Ruminal Acidosis (SARA), a disease known to induce changes in behaviour. Another 14 control cows were not submitted to SARA. We used a ruminal bolus to monitor pH and detect when a cow experienced SARA. We used a positioning system to infer an animal’s activity based on its position in relation to specific elements in the barn (feeder, resting area, and alleys). We tested several ML algorithms: K Nearest Neighbours for Regression (KNNR); Decision Tree for Regression (DTR); MultiLayer Perceptron (MLP); Long Short-Term Memory (LSTM); and an algorithm where activity is assumed to be similar from one day to the next. First, we developed ML models to predict activity on a given day from the previous 24 h, considering all cows together. Then, we calculated the error between observed and predicted values for a given cow. Finally, we compared the error to a threshold chosen to optimise the distinction between normal and abnormal values. KNNR performed best, detecting 83% of SARA cases (true-positives), but it also produced 66% of false-positives, which limits its use in practice. In conclusion, ML can help detect anomalies in behaviour. Further improvements could probably be obtained by applying ML on very large datasets at animal rather than group level. |
| Databáze: | OpenAIRE |
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