Invited review: Sensors to support health management on dairy farms

Abstract

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors

“…Many precision dairy-monitoring technologies claim to monitor udder health, estrus events, feet and leg health, and metabolic health (Rutten et al, 2013). These technologies provide benefit to producers and researchers by frequently monitoring dairy cattle without disturbing natural behavioral expression (Müller and Schrader, 2003).…”

The utility of sensor technology to support reproductive management on dairy farms

“…Studies on the performance of these systems reported comparable sensitivities and specificities for estrus detection, although gold standards varied (Rutten, et al, 2013). Because the gold standards differed, it cannot be concluded that performances are equal for these three sensor systems.…”

“…In addition, sensor technology has developed rapidly in recent years and one of the most applied technologies is automated estrus detection, although integrated decision support systems are lacking (Rutten, et al, 2013). Activity meters can improve estrus detection over the detection achieved by the farmer (Kamphuis, et al, 2012, Rutten, et al, 2014.…”

“…In recent years, many sensor systems have been proposed and evaluated for dairy cows (Rutten, et al, 2013). Such sensors could potentially be used as an alternative to visual observation for onset and progression of calving , Ouellet, et al, 2016, Rutten, et al, 2017, Titler, et al, 2015.…”

“…The activity meter has been the most widely studied (Rutten, et al, 2013) and might improve estrus detection compared to visual detection done by the farmer. Improved estrus detection increases the likelihood that a cow in estrus becomes pregnant and thereby shortens the calving interval, which is economically beneficial.…”