Goat Kidding Dataset

Abstract

The detection of kidding in production animals is of the utmost importance, given the frequency of problems associated with the process, and the fact that timely human help can be a safeguard for the well-being of the mother and kid. The continuous human monitoring of the process is expensive, given the uncertainty of when it will occur, so the establishment of an autonomous mechanism that does so would allow calling the human responsible who could intervene at the opportune moment. The present dataset consists of data from the sensorization of 16 pregnant and two non-pregnant Charnequeira goats, during a period of four weeks, the kidding period. The data include measurements from neck to floor height, measured by ultrasound and accelerometry data measured by an accelerometer existing at the monitoring collar. Data was continuously sampled throughout the experiment every 10 s. The goats were monitored both in the goat shelter (day and night) and during the grazing period in the pasture. The births of the animals were also registered, both in terms of the time at which they took place, but also with details regarding how they took place and the number of offspring, and notes were also added.

Exploring the Potential of Machine Learning Algorithms Associated with the Use of Inertial Sensors for Goat Kidding Detection

“…Functional and performance validation was carried out using data from a public dataset [ 38 ], with data from animal monitoring during parturition, in which the data were streamed to the system, imitating the functioning of the system under real conditions. Like this, the data were analyzed and the possibility of developing a real-time mechanism that could detect the change in goat behavior during their parturition process though the classification of streamed data was assessed to create an automatic kidding detector.…”

“…Data streams, such as the animal monitoring data stream [ 38 , 39 ], evolve over time in a stream of non-stationary distribution data and infinite size [ 40 ], as a function of the evolution of the monitored magnitude, such as the pitch angle of a goat’s neck. Sometimes animal behavior changes, for example because labor is starting, and this behavior cannot be modelled through the learning process carried out earlier, with the data obtained during the period before kidding.…”

SpaceSheep: Satellite Communications for Ovine Smart Grazing

“…SheepIT field-trial tests [ 6 ] demonstrated the importance of automating the shepherd’s job, as labor costs were reduced by freeing the shepherd to perform other tasks in the vineyard. In addition, the use of animal monitoring tools allows the identification and prediction of a wide range of animal-related events [ 6 , 7 , 8 , 9 ]. In [ 6 ], Gonçalves et al monitored the behavior of animals that were freely grazing throughout the day.…”

“…The results demonstrated the possibility of performing animal localization using a low-cost localization mechanism, although a localization error that depends on the location of the transmitter beacons was also found. The same monitoring infrastructure was used again, this time to create a dataset after monitoring goat kidding [ 7 ], as well as the nocturnal activity of sheep [ 10 ]. The analysis of the animals’ nocturnal activity data allowed Gonçalves et al [ 8 ] to verify the existence of a set of rest and activity cycles during the night, as previously reported in the literature.…”

SpaceSheep: Satellite Communications for Ovine Smart Grazing

“…SheepIT field-trial tests [4] demonstrated the importance of automating the shepherd's job, since labor costs were reduced by freeing the shepherd to realize other tasks in the vineyard. In addition, the use of animal monitoring tools allows identifying and predicting a very wide range of events related to animals [5][6][7][8].…”