Invited review: Toward a common language in data-driven mastitis detection research
Open Access
- 1 October 2021
- journal article
- review article
- Published by American Dairy Science Association in Journal of Dairy Science
- Vol. 104 (10), 10449-10461
- https://doi.org/10.3168/jds.2021-20311
Abstract
Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these ap-proaches try to predict mastitis events from (combina-tions of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it dif-ficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classifica-tion) for describing the different methods applied in sensor data-based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classifi-cation, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combina-tion of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that within approaches several different methods and terminologies for similar methods were used. Not all publications provided a clear description of the method used, and therefore it seemed that different methods were used between publications, whereas in fact just a different terminology was used, or the other way around. This paper is intended to serve as a reference for people from various research disciplines who need to collaborate and communicate efficiently about the topic of sensor-based mastitis detection and the methods used in this context. The framework used in this paper can support future research to correctly classify approaches and methods, which can improve the understanding of scientific publication. We encourage future research on sensor-based animal disease detection, including that of mastitis detection, to use a more coherent terminology for methods, and clearly state which technique (e.g., filtering) and approach (e.g., moving average) are used. This paper, therefore, can serve as a starting point and further stimulates the interdisciplinary cooperation in sensor-based mastitis research.Keywords
This publication has 63 references indexed in Scilit:
- Sensors and Clinical Mastitis—The Quest for the Perfect AlertSensors, 2010
- Simplify the interpretation of alert lists for clinical mastitis in automatic milking systemsComputers and Electronics in Agriculture, 2010
- Decision-tree induction to detect clinical mastitis with automatic milkingComputers and Electronics in Agriculture, 2010
- Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networksJournal of Dairy Research, 2009
- Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milkComputers and Electronics in Agriculture, 2008
- Mastitis detection in dairy cows by application of neural networksLivestock Science, 2008
- Analysing serial data for mastitis detection by means of local regressionLivestock Science, 2007
- Conceptual approaches for defining data, information, and knowledgeJournal of the American Society for Information Science and Technology, 2007
- Visual scoring of clots in foremilkJournal of Dairy Research, 2005
- Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filterComputers and Electronics in Agriculture, 1999