Dominiak, Katarina Nielsen

Hindsborg, Jeff

Kristensen, Anders Ringgaard

Pedersen, Lene Juul

English

Copenhagen University Research Information System

u n i ve r s i t y  o f  co pe n h ag e n  Københavns UniversitetArea-specific predictions of unwanted events using multivariate modeling of waterdataDominiak, Katarina Nielsen; Hindsborg, Jeff; Pedersen, Lene Juul; Kristensen, AndersRinggaardPublication date:2018Document versionPublisher's PDF, also known as Version of recordDocument license:UnspecifiedCitation for published version (APA):Dominiak, K. N., Hindsborg, J., Pedersen, L. J., & Kristensen, A. R. (2018). Area-specific predictions ofunwanted events using multivariate modeling of water data. 27-30. Paper presented at 2nd InternationalConference on Agro Big Data and Decision Support Systems in Agriculture, Lleida, Spain.Download date: 03. Feb. 2020Big DSS Agro 2018Area-specific predictions of unwanted eventsusing multivariate modeling of water dataKatarina N. Dominiak1, Jeff Hindsborg1, Lene J. Pedersen2, Anders R. Kristensen11 University of CopenhagenGrønnega˚rdsvej 2, 1870 Frederiksberg C, Denmarkknd@sund.ku.dk, ark@sund.ku.dk2 Aarhus UniversityBlichers Alle´ 20, 8830 Tjele, Denmarklene.juulpedersen@anis.au.dkAbstractIn this study simultaneously monitored drinking patterns from multiple pens in one weanersection and one finisher section herd are modeled in a multivariate dynamic linear model (DLM).Correlations between the monitored data are incorporated in the model allowing it to include in-teractions between pens in each section. A two-sided tabular CUSUM is applied as control chart,whereby alarms for outbreaks of unwanted events amongst the pigs can be generated for specific pensin the herd. Area-specific alarms enable the manager to include specific knowledge of the animals inthe targeted area and choose the best suited intervention.1 IntroductionThe primary cause of excess false alarms from sensor-based early warning systems for livestock pro-duction is difficulties at achieving detection performances so high that the number of false alarms will beacceptable in a real-life production herd, as discussed by [5, 7]. Therefore more focus should be aimed atdeveloping modeling methods or post-processing methods which rank, sort, or prioritize alarms beforethey are communicated to the manager.A new approach for prioritizing alarms from early warning systems is to relate alarms to a specificarea of the herd using a spatial detection model. This would allow the manager to include any specificknowledge of the animals in the targeted areas and choose the better intervention for preventing anoutbreak of an unwanted event or for reducing the consequences of one.Modern Danish production herds for growing pigs (weaners 7 - 30 kg and finishers 30 - 110 kg) arevery well suited for spatial modeling due to both the construction of the herds and the highly systematicmanagerial routines. The herds consist of identical sections which consist of a number of identical pens.Furthermore the pigs are inserted in the sections following a strategy where pigs of the same age areinserted in a section on the same day, and the section is emptied and disinfected before the insertion of anew batch of pigs [2].The objective of this paper is to validate a spatial model, which detects systematic changes in thewater consumption of growing pigs throughout the growth period. Area-specific alarms are generated bya control chart, and they will act as decision support in the daily management of the herd.2 Materials and methods2.1 DataThe model was originally developed on water consumption data from two different herds (Herd A, afinisher herd. Herd B, a weaner herd) where it was able to model simultaneously monitored data frommultiple pens in multiple sections of a herd. When applied it could identify a specific pen or a specificsection in a herd where an unwanted event was going to occur [4, 6].In the current paper, the model is validated on data from a single weaner section and a single finishersection in an independent herd (Herd C). Data sets from weaners and finisher are modelled separately.Lleida, July 12-14, 2018Big DSS Agro 2018The water data was monitored using photo-electric flow sensors (RS V8189 15mm Diameter Pipe)[1], which were installed in eight pens (four double pens) in a weaner section, and in sixteen pens (eightdouble pens) in a finisher section in the same herd. Data from Herd C was monitored while anotherproject was conducted in the herd. Therefore 50% of the pens in a section contained pigs with intact tailsand 50% of the pens contained pigs with docked tails. In every other batch, pigs with intact tails wereinserted in pens which contained pigs with docked tails in the previous batch and vice versa.Model validation was conducted following two strategies of internal and external evaluation. In Int.evaluation the first four batches of Herd C were defined as training data for the model, whereas the lasttwo batches of Herd C were defined as test data and used for performance evaluation. In Ext. evaluationdata from Herd A was used as training data for evaluation of all six batches from the finisher section ofHerd C, and data from Herd B was used as training data for evaluation of all six batches from the weanersection of Herd C.Every morning, the caretaker registered events of diarrhoea and tail biting in each pen. These eventregistrations constitute the gold standard when evaluating model performance. Evaluation was con-ducted for the prediction of either of the two events separately and for both events together.2.2 General modelThe water consumption over time was modelled simultaneously for all sensors in each section using amultivariate DLM as described by [10]:The observation vector Yt = (Y1t, . . . , Ynt)′ , contains the observation at time t for each of the n sensors.Both the relation between Yt and the underlying parameter vector θt at time t, and the evolution of thesystem over time, are described through an observation equation and a system equation (Equations (1)and (2) respectively):Observation equationYt = F′tθt + νt, νt ∼ N (0, Vt), (1)andSystem equationθt = G′tθt−1 + wt, wt ∼ N (0, Wt). (2)The overall aim of the DLM is to predict the next observation of the monitored variable by estimatingthe parameter vectors, θ1, . . . , θt, from the observations, Y1, . . . , Yt. Every observation is added to themodel’s prior knowledge of the modeled system, and this dynamic updating enables the model to predictthe next observation with increased certainty over time. When a new observation is made, the predictedvalue and the observed value are compared, and any differences between the predicted and the actualobservations are due to the two error terms, νt and wt [3].If the pigs in the pens follow their normal drinking pattern and drink the expected amounts of water,the prediction of the next observation is close to perfect, and any prediction error will be small. If, onthe other hand, something cause the pigs to drink more or less than expected, the prediction error will belarger. A systematic change in the normal drinking pattern will generate a sequence of larger predictionerrors, which will be reflected as an alarm generated by a CUSUM.2.3 Modeling diurnal patternsA previous study by [8] found, that three harmonic waves of lengths 24h, 12h, and 8h describe the diurnalpattern, which characterize the water consumption of growing pigs. Each harmonic wave is expressedas cyclic models in a dynamic linear model through the trigonometric Fourier form representation ofseasonality [10] as:Lleida, July 12-14, 2018Big DSS Agro 2018Fht =(10)and Ght =(cos(ω) sin(ω)− sin(ω) cos(ω)). (3)with ω = 2pi/24 yielding a wave with a period of 24h, ω = 2pi/12 a wave with a period of 12h, andω = 2pi/8 a wave with a period of 8h.However, since pigs drink more water as they grow, a trend must be added to the diurnal pattern inorder to model the full drinking pattern. Therefore a dynamic linear growth model was used for modelingthe underlying level of water consumed as well as the increase in the level from time t − 1 to t. It isdescribed by [10] as:Flt =(10)and Glt =(1 10 1). (4)2.4 Area-specific alarmsDetection of alarms and irregular drinking patterns was done using Tabular CUSUM control chart asdescribed by [9]. The Cusum generated cumulated sums of the positive and the negative forecast errorsseparately over time, and plotted them as Upper Cusum and Lower Cusum respectively. If either of theCusums exceeded a defined threshold the monitored process was considered out of control, and an alarmwas generated.A measure of test accuracy, the Area Under Curve (AUC) was calculated in order to evaluate themodel performance. This was done separately for the two sections. The detection model was evaluatedfor the ability to predict events in a specific pen in the section by comparing the generated alarms withthe gold standard registrations from the personnel. Time windows applied began two days before theevent and included the day of the event as well.3 ResultsThe accuracies for predicting an event in a specific pen were high following both evaluation strategies(see Table 1). The prediction of tail bites separately obtained the highest performance for both sectionsand evaluation strategies, whereas the prediction of diarrhea separately performed the poorest. Somedifferences in the AUC can be seen between the two evaluation strategies. However, the relatively smalldifferences implies that variance estimates generated on data from one herd can be successfully appliedto data from other herds.Weaners FinishersEvent Int. evaluation Ext. evaluation Int. evaluation Ext. evaluationTail bites 0.92 0.88 0.84 0.83Diarrhea 0.80 0.77 0.72 0.71Both events 0.81 0.81 0.73 0.76Table 1: AUC (area under curve) for prediction of either tail biting, diarrhea or both events in specificpens in a section of finishers and a section of weaners following two evaluation strategies; Int. evaluationand Ext. evaluation4 Conclusion and perspectivesThe detection accuracies obtained in this study implies that changes in drinking patters are highly in-formative for the prediction of tail bites, and to some extend outbreaks of diarrhea amongst growingpigs. The findings furthermore supports the results found in [4] which implies that the alarms are veryLleida, July 12-14, 2018Big DSS Agro 2018accurate in pointing out a specific area where unwanted events are about to occur. This means that whena manager receives an alarm for a specific area in the herd, it is very likely that an event is going to occurin that area within the next two or three days. Due to the systematic managerial routines in the herd,the manager knows the age and size of pigs in any section at any time, as well as high-risk periods foroutbreaks of specific events. This means that an area-specific alarm can provide a valuable support forthe manager in deciding both which pen or section to attend first and which intervention to implement.Although the full potential of the model is better exploited when applied to data from multiple pens inmultiple sections, the present study clearly shows that the model is robust and can be applied to a widespan of data sets.5 AcknowledgementsThis research was funded by the Danish Council for Strategic Research (The PigIT project, Grant number11-116191). We further wish to thank the anonymous farmers, the Danish Pig Research Centre and thetechnical staff (particularly Mads Ravn Jensen) at Aarhus University for installing and supervising thesensors and for taking care of the daily observations in the herd.References[1] Anonymous. Instruction leaflet flow sensors - 15 mm dia. pipe v8189, 2000.[2] R. D. A. Cameron. A review of the industialisation of pig production worlwide with particularreference to the asian region. Food and Agriculture Organization of the United Nations, 2000.[3] K. N. Dominiak. Spatial modeling of drinking patterns as a tool for reducing alarms in pig pro-duction. PhD thesis, The Graduate School of Health and Medical Sciences, University of Copen-hagen, 2017.[4] K. N. Dominiak, Hindsborg J., L. J. Pedersen, and A. R. Kristensen. Spatial modeling of pigs’drinking patterns as an alarm reducing method. II. Application of a multivariate dynamic linearmodel. Submitted for journal publication, 2018.[5] K. N. Dominiak and A. R. Kristensen. Prioritizing alarms from sensor-based detection models inlivestock production - a review on model performance and alarm reducing methods. Computersand Electronics in Agriculture, 133:46–67, 2017.[6] K. N. Dominiak, L. J. Pedersen, and A. R. Kristensen. Spatial modeling of pigs’ drinking patternsas an alarm reducing method. I. Developing a multivariate dynamic linear model. Accepted forjournal publication, 2018.[7] H. Hogeveen, C. Kamphuis, W. Steeneveld, and H. Mollenhorst. Sensors and clinical mastitis - thequest for the perfect alert. Sensors, 10(9):7991–8009, 2010.[8] T. N. Madsen, S. Andersen, and A. R. Kristensen. Modelling the drinking patterns of young pigsusing a state space model. Computers and Electronics in Agriculture, 48(1):39–62, 2005.[9] Douglas C. Montgomery. Statistical Quality Control A Modern Introduction. John Wiley & Sons,Inc, seventh edition, 2013.[10] Mike West and Jeff Harrison. Bayesian Forecasting and Dynamic Models. Springer, 2nd edition,1999.Lleida, July 12-14, 2018

Area-specific predictions of unwanted events using multivariate modeling of water data

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Area-specific predictions of unwanted events using multivariate modeling of water data

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