Automatic detection of oestrus and health disorders using data from electronic sow feeders

Cornou, Cécile; Vinther, Jens; Kristensen, Anders Ringgaard

doi:10.1016/j.livsci.2008.02.004

Cited by 64 publications

(51 citation statements)

References 20 publications

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“…Cornou et al (2008) used data from electronic sow feeders to determine the individual feeding rank of sows. For growing-finishing pigs, electronic feed stations have been developed and used to log time and duration of feed visits (Hyun et al, 1997;Bruininx et al, 2001a;Bruininx et al, 2001b;Hyun and Ellis, 2002).…”

Section: Introductionmentioning

confidence: 99%

Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs

Maselyne

Saeys

Ketelaere

et al. 2014

Computers and Electronics in Agriculture

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Section: Introductionmentioning

confidence: 99%

Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs

Maselyne

Saeys

Ketelaere

et al. 2014

Computers and Electronics in Agriculture

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“…For growing-finishing pigs electronic feed stations have been developed and used to log time and duration per feeding visit as well as the feed intake of the individual pig (Hyun et al, 1997;Bruininx et al, 2001aBruininx et al, , 2001bHyun and Ellis, 2002). Electronic sow feeders have been used in research (Cornou et al, 2008) as well as in practice (pig farmers with group housing of gestating sows) (Tuyttens et al, 2011). However, these feeders only allow access to one pig at a time and mostly only one feeder is available per group.…”

Section: Introductionmentioning

confidence: 99%

Range measurements of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing–finishing pigs

Maselyne

Nuffel²,

Ketelaere

et al. 2014

Computers and Electronics in Agriculture

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“…Rather than estimating W t , the system variance was modeled using a discount factor (δ), as previously described by [22] and [17]. …”

Section: Methodsmentioning

confidence: 99%

A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases

Antunes¹,

Jensen²,

Halasa³

et al. 2017

PLoS ONE

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Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease. The objectives of this study were to assess the performance of different statistical monitoring methods for endemic disease control program scenarios, and to explore what impact of variation (noise) in the data had on the performance of these monitoring methods. We simulated 16 different scenarios of changes in weekly sero-prevalence. The changes included different combinations of increases, decreases and constant sero-prevalence levels (referred as events). Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms–Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring of the trend component–based on 99% confidence intervals and the trend sign) were tested. Performance was evaluated based on the number of iterations in which an alarm was raised for a given week after the changes were introduced. Results revealed that the Shewhart Control Chart was better at detecting increases over decreases in sero-prevalence, whereas the opposite was observed for the Tabular Cumulative Sums. The trend-based methods detected the first event well, but performance was poorer when adapting to several consecutive events. The V-Mask method seemed to perform most consistently, and the impact of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods. The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence. Combining two of more methods might improve the potential scope of surveillance systems, allowing them to fulfill different objectives due to their complementary advantages.

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Automatic detection of oestrus and health disorders using data from electronic sow feeders

Cited by 64 publications

References 20 publications

Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs

Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs

Range measurements of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing–finishing pigs

A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases

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