An intelligent control chart for monitoring of autocorrelated egg production process data based on a synergistic control strategy

Mertens, Kristof; Vaesen, I.; Löffel, Jenny; Kemps, Bart; Kamers, B.; Zoons, Johan; Darius, Paul; Decuypere, Eddy; Baerdemaeker, Josse De; Ketelaere, Bart De

doi:10.1016/j.compag.2009.07.012

Cited by 26 publications

(46 citation statements)

References 11 publications

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“…To monitor the course of the TCV and detect aberrations as a result of such phenomena, the algorithm presented by Mertens (2009) and Mertens et al (2008Mertens et al ( , 2009) was used to construct a control chart for the course of the TCV. This algorithm develops a curve based on the actual in-control observations of TCV on a certain time in the production period.…”

Section: Discussionmentioning

confidence: 99%

“…This group of laying hens was followed daily during in total 360 d of lay. The first day of lay (DOL) is considered to be the time point at which the hen-day egg production reaches more than 40% (Mertens, 2009;Mertens et al, 2009). each day, all of the eggs were collected, percentage of second-grade eggs (broken, cracked, and dirty eggs) was defined by visual inspection, and all eggs were weighed collectively to define the average egg weight.…”

Section: Methodsmentioning

confidence: 99%

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The transmission color value: A novel egg quality measure for recording shell color used for monitoring the stress and health status of a brown layer flock

et al. 2010

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Stress and diseases have the potential to influence the deposition of eggshell pigmentation during egg formation. Therefore, defining the shell color of eggs on a daily basis could be a representative method for monitoring stress or health status of a flock and maintaining good performance. A novel way of measuring eggshell color based on visible-near infrared transmission spectroscopy transmission spectra was defined: the transmission color value (TCV). The TCV was calculated as the ratio between the transmission at 643 nm (maximum absorbance of the pigmentation molecule protoporphyrin IX) and the transmission at 610 nm (a reference wavelength). Experiments were carried out to investigate the relevance of TCV for monitoring flock stress and health or even anticipating any factors unfavorable to performance. In 2 small experimental flocks, deliberate heat stress challenges were applied. A medium-scale experimental flock in an aviary was monitored on a daily basis during the whole productive period. From the deliberate heat stress challenges, it was seen that stress had a significant effect on eggshell pigmentation. This observation was confirmed in a daily monitored flock, in which, for example, an infectious bronchitis infection occurred. These stress situations were quickly reflected by an increased TCV value: more transmission due to less pigmentation and hence less absorbance at the pigmentation wavelength. Furthermore, for the observed problems in the daily monitoring, the TCV value signaled the problem earlier (4 d) than the average egg weight or even signaled when the other parameters did not signal anything. Measuring the TCV of all eggs produced on a daily basis provides relevant information on the stress or health status of a flock of brown layers. This could be used as an early detection of stress situations or emerging diseases, even before important quality and health damage can occur.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The transmission color value: A novel egg quality measure for recording shell color used for monitoring the stress and health status of a brown layer flock

et al. 2010

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“…Compared to these results, the model proposed in this paper obtained an SEN of 0.8639 and an SPC of 0.9954 along with an ACC of 0.9925 and a PPV of 0.8125 for the day prior to the anomaly. Mertens et al (2009) also proposed the development of a system based on an intelligent control chart for monitoring the egg production process. This system provided alarms for problems in the production curve for the same day that production dropped.…”

Section: Performance Analysismentioning

confidence: 99%

“…Data analysis in poultry systems has mainly been performed using mathematical methods (Frost et al 1997), statistical techniques (Narinc et al 2014) and visual graph analyses (Mertens et al 2009). These methods allow for the identification of anomalies in production by pointing out important differences among the production indices (De Vries and Reneau 2010).…”

Section: Introductionmentioning

confidence: 99%

Automated early detection of drops in commercial egg production using neural networks

Ramírez-Morales

Fernández-Blanco

Rivero

2017

British Poultry Science

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ABSTRACT1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of costsensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.

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“…This scheme detects the two-sided mean shift δ μ and increasing standard deviation shift δ σ based on a single statistic WL (the weighted loss function). Mertens et al [9] monitored the livestock production processes by means of statistical control charts. The non-stationary and autocorrelated characteristics of most data originating from such processes impeded the direct introduction of these data into control charts.…”

Section: Litreture Reviewmentioning

confidence: 99%

Effectiveness of Conventional CUSUM Control Chart for Correlated Observations

Prajapati¹

2015

IJMO

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Abstract-Control charts, one of the important tools of quality control, are also known as Shewhart charts or process behavior charts. Page (1954) was the first, who introduced the Cumulative Sum (CUSUM) control charts for detection of process shifts and claimed that these charts are more efficient compared to Shewhart chart to detect small shifts in the process average. Various schemes of the CUSUM chart for autocorrelated data for sample size of 4 are developed and compared with the schemes of the Shewhart X chart for autocorrelated data. It is found that CUSUM chart outperforms the Shewhart X chart for all the shifts and at all the levels of correlation (Φ) for sample size (n) of four. So, the CUSUM control chart is much better option for faster detection in the process mean.

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An intelligent control chart for monitoring of autocorrelated egg production process data based on a synergistic control strategy

Cited by 26 publications

References 11 publications

The transmission color value: A novel egg quality measure for recording shell color used for monitoring the stress and health status of a brown layer flock

The transmission color value: A novel egg quality measure for recording shell color used for monitoring the stress and health status of a brown layer flock

Automated early detection of drops in commercial egg production using neural networks

Effectiveness of Conventional CUSUM Control Chart for Correlated Observations

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