Comparison of Machine Learning Methods to Detect Anomalies in the Activity of Dairy Cows
| Autor: | Marie-Madeleine Mialon, Nicolas Wagner, Isabelle Veissier, Romain Lardy, Violaine Antoine, Jonas Koko |
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| Přispěvatelé: | Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), 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 (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), 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) |
| Rok vydání: | 2020 |
| Předmět: |
2. Zero hunger
Time series classification business.industry Computer science Deep learning Anomaly (natural sciences) 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Production efficiency Machine learning computer.software_genre Residual neural network [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Boss 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030594909 ISMIS Comparison of Machine Learning Methods to Detect Anomalies in the Activity of Dairy Cows Comparison of Machine Learning Methods to Detect Anomalies in the Activity of Dairy Cows, pp.342-351, 2020, ⟨10.1007/978-3-030-59491-6_32⟩ |
| Popis: | Farmers need to detect any anomaly in animals as soon as possible for production efficiency (e.g. detection of estrus) and animal welfare (e.g. detection of diseases). The number of animals per farm is however increasing, making it difficult to detect anomalies. To help solving this problem, we undertook a study on dairy cows, in which their activity was captured by an indoor tracking system and considered as time series. The state of cows (diseases, estrus, no problem) was manually labelled by animal caretakers or by a sensor for ruminal pH (acidosis). In the present study, we propose a new Fourier based method (FBAT) to detect anomalies in time series. We compare FBAT with the best machine learning methods for time series classification in the current literature (BOSS, Hive-Cote, DTW, FCN and ResNet). It follows that BOSS, FBAT and deep learning methods yield the best performance but with different characteristics. |
| Databáze: | OpenAIRE |
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