- Email: [email protected]
Behaviour of Chickens Towards Automatic Weighing Systems
Copyright ® IF AC Bio-Robotics, Information Technology and Intelligent Control for Bio-Production Systems, Sakai, Osaka, Japan, 2000
BEHAVIOUR OF CIDCKENS TOWARDS AUTOMATIC WEIGIDNG SYSTEMS
A. Cbedad, E. Vranken, J.-M. Aerts, D. Berckmans(l)
(I)
Laboratory for Agricultural Buildings Research, Katholieke Universiteit Leuven, Kardinaal Mercierlaan 92, 3001 Leuven, Belgium Corresponding author, e-mail: [email protected]. te!. +32-16-32. 14. 36,fax +32-16-32.14.80
Abstract: In previous studies it was found that automatic broiler weighing systems deal with some accuracy problems. Researchers reported poor agreement between automatic and manual mean weighing used as a reference. The difference was observed especially after 4 weeks and this was explained by assuming that the weighing system is less visited by heavier chickens at the end of the production period. The objective of the research reported in this paper was to test this hypothesis under practical production conditions. From the image analysis, the higher formulated hypothesis that 'the system was used less frequently by heavier animals' could be confirmed. Copyright © 2000 IFAC Keywords: Image Analysis, Monitoring, Behaviour
1.
tarred, so that faeces and litter are weighed with. After a number of weighing, a new average is calculated and the above and below limit re-adapted. Once calibrated, the system has an automatic tare weight. To prevent two birds being recorded at one time, a number of readings of each individuals birds are averaged and must be within +1- 10 % of each other to be valid (Anonymous, 1982). The implementation of a computerised weighing system has resulted in a substantial reduction in the amount of time (60 to 65%; Feighner, et al., 1984) necessary for data acquisition and analysis. At the end of each run the following information is presented either on the screen or on the printer: date, start time, duration run, total number of weights recorded, mean weight, standard deviation, number recorded in various weight bands or intervals, and a list of the individual weight recordings in order of recording.
INTRODUCTION
It is of major importance to the flock manager to
have on-line information about weight, uniformity and growth of the birds, feed conversion efficiency (Newberry, et al., 1985; Wolterink, and Meijerhof, 1989), occurrence of disease problems and vitality (Meijerhof, 1989), and also to be able to forecast some days in advance the average weight and spread of weights at slaughter (Turner, et al., 1984). An accurate prediction of not only average weight but also the range of weight distribution throughout the flock can be of valuable assistance to the producer planning his killing program and scheduling broiler pick-up and slaughter (Ross, et al., 1990). Determining broilers mean body weight in an on-line way is done today by automatic weighing: 1.2 Automatic weighing
However, automated broiler weighing systems still have some implementation problems that need to be overcome. For example, the design of perch is fairly critical in that the perch must attract high, representative numbers of birds each day but not be so acceptable that birds having mounted it stay for long periods (Turner, et aI., 1981).
Automatic weighing involves introducing one or more portable platform balances into the broiler house that are connected to a data analyses unit. The output from the platform is processed so that the weight of any broiler getting on the perch and staying there for a few seconds is recorded. The computer program used for weight recording provides for automatic recording of the weight of the empty balance so that this can be subtracted from the weight of an occupied balance to arrive at the weight of the broiler. After weighing, the balance is automatically
Some researchers obtained poor agreement between automatic and manual mean weighing. Newberry, et al. (1985) and Blokhuis, et al. (1988) found that the automatic body weights were lower than the manual
207
metallic frame at a distance of 0.9 m above the floor and was connected to a video recorder. The distance to the floor was taken similar in the different experiments in order to be able to compare the images of different experiments. The camera was equipped with a telephoto lens (Cosmicar 4.8 mm 1: 1.16) to allow the desired area of view to be captured. The automatic weighing system was placed on the floor under the CCD camera. The measured data from the weighing system were stored on a Pc. It was possible from this height to observe the animals on a surface of circa 2.3 m x 1.6 m. Stable (1) (CLO) was lighted with commonly used TLlamps (Phillips 36 W) and produced a light intensity of 194 lux which was measured on 6 random places in the stable. This illumination resulted in remarkable quality of the recorded images. Besides the good illumination, the floor was covered with shopped straw, which improves the contrast between the animal and the background. The stable had a dimension of 11.6 m x 16.2 m with a total of 2900 birds at a stocking density of 16 birds/ m 2 (mixed gender, genetic strain: Ross). Four automatic weighing systems were placed between the feederand drink station. The food and water were automatically administered.
means. This difference seems higher in the fifth week compared to week three. Different flocks show somewhat different deviations, varying between 1.5 and 5.5%. It was summarised by Hughes, et al. (1977), that there was a tendency for frequent perchers to be lighter than infrequent ones. The difference was observed especially in week 6 (Blokhuis, et aI., 1988). Blokhuis, et aI., (1988) , explained this by assuming that the weighing system was less visited by heavier birds at the end of the production period. Experience of WoIterink, et aI., (1989), is that such a weighing system works good with broilers at a young age but in the period of the fmishing phase the scales are visited less frequently. Although these systems give indications about the relative path of the growth curve of the flock but the exactness of the measurements seems still doubtful, especially at the end of the growing period (Blokhuis, et aI., 1988). The objective of this paper is to test the above mentioned hypothesis: that the weighing system is less visited by heavier chickens at the end of the production period formulated by Blokhuis, et aI., ( 1988). 2.
MATERIAL AND METHODS
2.1 Determining weight by digital image processing Another alternative is to estimate broiler chicken live weight from its dimensions by means of image analysis techniques. This method was effectively used in the estimation of the bodyweights (Schofield, et aI., 1990; Ali, et al., 1993; Marchant, and Schofield, 1993) and carcass characteristics (Van der Stuyft, 1994) of pigs. Under interactive operator control, the live weight of pigs could be determined within +/- 3 % of the actual weight, if poor pictures were discarded (Schofield, et aI., 1999). Under optimal lighting conditions, De Wet, et al. (1998), developed an imaging system for individual broilers to determine the body weight from its dimension (upper view) in pixels. The weight increases proportional with the surface area (a second-degree function). A high correlation (R = 0.98) was obtained between the surface area pixel value from the upper view and their weight.
Fig.l. Schematic representation of the experimental set-up. Stable (2) (PDLT) was set to an illumination of 5 lux. This illumination was too weak to extract relevant information form the images. An extra light source (Philips 350 W) was mounted at a height of 2.3 m above the ground and produced a light intensity of 174 lux in the neighbourhood of the scale. The stable had a dimension of 15 m x 10 m and was divided into four equal parts with a total of 1500 birds at a stocking density of 20 birds/ m 2 (mixed gender, genetic strain: Ross). In each part, an automatic weighing system was placed between the feeder- and drink station. The imaging system was placed above one of those platforms.
2.2 Experimental set up In order to test the higher formulated hypothesis, images from broilers on and around the scale had to be collected during a production period (42 days). The experiments were performed on 3 locations, namely at the "Provenciale Dienst van Land en Tuinbouw, Antwerpen" (PDLT), "Centrum van Landbouwkundig Onderzoek, Merelbeke" (CLO) and the "Zootechnisch Centrum, Lovenjoel" (ZTC). A CCD "Charged coupled device" monochrome camera (KP-MIEIK HITACHI) was mounted on a
208
In a picture represented by L grey levels [1,2, ... ,L], the pixels at level I is denoted by nj and the total number ofpixels by N = nl +n2 + .. . nL.
Finally, stable (3) (ZTC) was set to an illumination of 199 lux, which produced images from a good quality. The 50 birds were kept on wood shavings and occupied 3 m x 4.3 m. It was possible to keep 16 animals per m 2• The images were stored on VHS video tapes and were digitised on 486DXl40 PC fitted with a frame grabber (Matrox, PIP-I024B) capable of capturing a 256 x 256 pixels image. The imaging system registered 4 to 8 times a day during 10 minutes each. During each measurement period, 16 images were digitised 5 times every 2 minutes and were stored in a file named with the date and time of the measurement so that 80 digitised images were obtained after one measurement period of 10 minutes.
To evaluate the "goodness" of the threshold level k, Otsu introduces the discriminate criterion which is determined as the between-class variance
O"~(k) =aJo(k)[uo(k)- liT ]2 + aJdk)[udk )- liT ]2 [lIraJ(k) - ,u(k)f aJ(k)[l- aJ(k)]
where O"p(k) denotes the between-class variance of the background and object, aJo (k), aJI (k ) : probability of class occurrence of the background and object, respectively, 110 (k ),111 (k): probability of class mean level of the background and object, respectively and IIr (k): the total mean level of the picture.
2.3 Method Correction ofNonunifonn illumination. In some parts of the images the object are brighter than in other parts. Let a(iJ) be an image with iJ E [O,255j.
The optimal threshold k • is given by the maximum argument (Argmax) defmed by
The following procedure is used to fmd a coarse estimate of the background illumination, by determining the minimum of each 32 x 32 block in the image. Let I(iJ) be a 3 dim surface of the nonuniforme illuminated image a (iJ). The values of I(iJ) are calculated by determining the minimum of each 32 x 32 block in the image or: I(i,j)
= Min
a(i,})
i,} E32x32
~p(e) = max ~p(k)
= a(i,}) -
I(i,})
i,} E(0,255]
(4)
l~k~L
The method described has low computational load due to the exploitation of only the zero-and firstorder cumulative moments of the histogram up to the level k. Calculation time can also be reduced by restricting the range of k over which the maximum is sought from the application.
(1)
The coarse estimate is then expanded in size so that it has the same size as the original image. The computed illumination is subtracted from the original image to correct for the nonuniformity or: d(i,})
(3)
Label Algorithm. Once a gray level image has been processed to remove noise and thresholded to produce a binary image, a connected components labeling operator can be employed to group the binary-l pixels into maximal connected regions (Haralick and Shapiro, 1992). Its input is a binary image and its output is a symbolic image in which the label assigned to each pixel is an integer uniquely identifying the connected component to which that pixels belongs. Once the regions where labelled, a binary erosion followed by a dilatation was appllied.
(2)
Unfortunately, these operations also darken the broilers on the image. By rescaling the pixel intensities, the entire intensity range is filled. This makes the broilers brighter, with more visible details. Automatic Thresholding: "Otsu Method". Since there is a continuous change in contrast between the animal and background during the production period (the feather of the broilers are yellow on a yellow like background at the beginning of the production. At the end the feather became white and the background more black like), an automatic threshold method is needed. This method determines an automatic threshold as Ling, et aI., (1996), for measuring the canopy for tomato seedling describes it. The above conditions motivated the selection of an adaptive thresholding technique for the segmentation task. The Otsu method selects an optimal threshold value based on the discriminate criteria that evaluates a "goodness" of the threshold at any level (Otsu, et
Algorithm. The algorithm for calculating the area of the animals that visited the scale and those, who did not, consists of the procedures mentioned above. It consists of the following steps: 1. A file is loaded. Every file consists of 16 digitised images of (256 x 256) pixels. 2. Selection of area around the balance. 3. The nonuniform illumination of the image is calculated and corrected. 4. Calculating of the optimal Threshold "Otsu method". 5. Construction of a binary image.
al.,1979) .
209
The algorithm for calculating the area of the animals that did not visit the scale is similar except that a bigger area around the weighing system is selected (1m2) . A flow chart of the segmentation algorithm is shown in figure 2.
Selection of the area around the weighing system
3. Correction of non uniform illumination
RESULTS AND DISCUSSION
The average values for surface area pixels were determined for broilers that jumped on the scale and those, who did not. In order to perform the surface estimation accurately, it is essential to calculate the average surface over sufficient images from the same animal. For that, 16 images were taken from the same animal. The averages for the surface area and standard deviation for the animals that came up the scale and those, who did not, are represented in table 1. At stable (1), 200 broilers were weekly manual weighed for reference. The weight obtained after day 36 and 42 by means of the manual weighing and automatic weighing was respectively (1892 ± 40; 2124 ± 10) and (2350 ± 34; 2555 ±20). From results obtained, it is clear that the weight measured by the automatic scale is underestimated from day 36. In figure 3, the average surface of the animals that visited the weighing system (full line) and those that were within the selected area (dotted line) around the weighing system is plotted in function of time. The deviation between both lines became clear from approximately day 36 of the production period.
no
Construction of a binary image
Regions in the image are labelled.
no
Calculation of daily mean area en stdv.
Area ofbroil"" that vi sted the balance .. •.. Area of chickens around the balance
3000
' 000 500
Fig. 2. A flowchart of the developed segmentation algorithm. 6. 7. 8.
9.
'0
20
25
30
'5
lime(doys)
Segments or region are labelled. The connected components labelling algorithm is applied. On each segment, binary erosion followed by a dilatation was applied. The area of the regions are calculated. If the value is within an acceptance range, it will be stored on the disk. Areas that are in touch with the boundaries are eliminated. The daily average area and standard deviation are calculated from the captured images.
Fig. 3. Surface area of broilers that visited the weighing system (full line) and those who did not (dotted line). Data from CLO.
210
Table 1. Daily mean area with standard deviation of broilers that visited the scale and those. who did not
Day
15 16 17 18 22 23 24 25 26 29 30 31 32 33 34 36 37 38 41 42
Surface area of broiler (data CLO) (Mean + Stdev) Surface of Surface of broilers on broilers around the the balance balance 607 ± 15 628 ± 50 611, ± 22 611 ± 33 616 ± 36 930 ±9 682 ± 34 619 ± 35 944 ± 45 918 ± 25 946 ± 54 912 ± 22 997 ± 52 978 ± 26 1062 ± 88 1176±61 1077 ± 26 1088 ± 35 1170 ± 48 1034 ± 51 1161 ± 18 1091 ± 41 1432 ± 74 1224 ± 9 1370 ± 56 1401 ± 92 1298 ± 87 1487 ± 96 1518±24 1526±81 1705 ± 8 1680 ± 12 1909 ± 27 1633±15 1868 ± 47 1998 ± 97 2387 ± 05 2110 ± 21 2179 ± 58 2857 ± 91
Surface area of broiler (data PDLT) (Mean + Stdev) Surface of Surface of broilers broilers on around the the balance balance 567 ±4 590 ± 12 623 ± 44 604 ± 51 702 ± 25 685 ± 17 714 ± 34 720 ± 41 920 ± 40 890 ± 72 917±41 933 ± 32 912 ± 29 902 ± 54 1040 ± 51 994 ± 31 1127 ± 62 1056 ± 47 1214±28 1280 ± 43 1264 ± 54 1278 ± 74 1302 ± 16 1231 ± 25 1380 ± 23 1282 ± 11 1467 ± 65 1350 ± 26 1540 ± 14 1459 ± 24 1602 ± 58 1508±89 1695 ± 67 1556 ± 47 1720 ± 25 1621 ± 78 2704 ± 47 2136 ± 84 2965 ± 41 2235 ± 71
ACKNOWLEDGEMENTS
Since the weight increases proportional with the 2D surface area (De Wet, et a/., 1998), it can be concluded that the animals around the balance are heavier than those, which come up the weighing system, which means that the weight measurements are under estimated. This explains the low measurement accuracy of an automatic weighing system. It must be clear that weighing system is accurate (1 gram) but the fact that lighter birds are coming up at the end of the production period, makes this measurement technique inaccurate. From figure 4, it is clear that there was a tendency for frequent percher to be lighter than infrequent ones. The difference is observed especially from day 36 (week 6, table 2) as it was found by Blokhuis, et a/. (1988). It is also clear from table 1 that the weighing system works well for broilers until the age of 36 days, as found in literature by Wolterink, et a/. (1989).
4.
Surface area of broiler (data ZTq (Mean + Stdev) Surface of Surface of broilers on broilers the around the balance balance 520 ± 23 576 ± 51 614 ± 51 602 ± 24 656 ± 74 632 ± 47 670 ± 26 612 ± 12 726 ± 33 701 ± 36 820 ± 12 814 ± 44 852 ± 51 822 ± 15 890 ± 54 862 ± 56 930 ± 41 945 ± 41 944 ± 23 987 ± 26 1077 ± 18 999 ± 58 1023 ± 61 1045 ± 78 1156 ± 24 1103±81 1254 ± 41 1198 ± 63 1347 ± 47 1477 ± 71 1401 ± 61 1516 ± 10 1478 ± 76 1656 ± 15 1521 ± 81 1750 ± 66 2314 ± 67 1888 ± 27 1957 ± 63 2465 ± 57
The authors are grateful to the Ministry of Small Enterprises and Agriculture (Belgium) for fmancial support. The PDLT and CLO provided field trial facilities and assistance in gathering data, and the authors are grateful to Ir. 10han Zoons from PDLT and Ir. Marijke Lippens from CLO for their help and co-operation.
REFERENCES Ali, N.M. (1993) . Variance in pigs dimensions as measured by image analysis. Livestock environment IV, Fourth International Symposium University of Warwick Coventry England, American Society ofAgricultural Engineers, Michigan, 151-158. Anonymous (1982). Live bird weighing simplified, Poultry International, 6:44-50. Blokhuis, HJ., J.W. Van Der Haar, and J.M.M Fuchs (1988). Do weighing figures represent the flock average?, Poultry International, 4(5), 17-19. De Wet, L., E. Vranken and D . Berckmans (1998). Determination of daily broiler bodyweight changes using computer-assisted image analysis. Proceedings of the World Poultry Science Association Conference held on October 16th, 1998 at Pretoria, South Africa, 19 p.
CONCLUSIONS
The hypothesis that "the weighing system is less visited by heavier broilers at the end of the production period" formulated by Blokhuis, et a/. (1988) and W olterink, et al. (1989) was confirmed. The difference was observed from day 36 with an average deviation of 19,4 % between the top vieuw area of broilers that came up the weighing system and those who did not (table 1).
21 1
Van der Stuyft, E. (1994) Exterieurwaarden·ng bi} levende varkens gebaseerd op driedimensionale digitale beeldanalyse, Phd Thesis Nr. 255 at the faculty of Agricultural and Applied Biological Science, K.U.Leuven, 318p.
Doyle, I. and S. Leeson (1989). Automatic weighing of poultry reared on a litter floor, Canadian Journal ofAnimal Science, 69, 1075-1081. Feighner, S.D., E.F. Godowsky and B.M. Miller (1986). Portable microcomputer-based weighing systems: Applications in poultry science, Poultry Science, 65: 868-873. Haralick, R.E. and L.G. Shapiro (1992). Computer and Robot Vision. Addison-Wesley Publishing Company.'New York. Hughes, B.O. and H.A. Elson (1977). The use of perches by broilers in floor pens. British Poultry Science, 18: 715-722. Wolterink, K.H. and R. Meijerhof(1989). Dierweegsysteem geschikt voor opfok, Pluimveehouderi} 6 Januari 1989,19:16-17. Ling, P.P. and V.N. Ruzhitsky, (1996). Machine vision techniques for measuring the canopy of tomato seedlings, Journal of Agricultural Engineering Research, 65, 85-95. Lokhorst, C. (1996). Automatic weighing of individual laying hens in aviary housing systems, British Poultry Science, 37: 485-499. Marchant, J.A. and c.P. Schofield, (1993). Extending the image process algorithm for outlining pigs in scenes. Computers and electronics in agriculture, 8,261-275. Meijerhof, R. (1989). Automatishe dierweegsystemen bieden voordeel, 'Automtical weighing system gives advantages' , Pluimveehouderri} 25 Augustus 1989, 19: 12-13. Newberry, R.C., lR. Hunt and E.E. Garriner (1985). Behaviour of roaster chickens towards an automatic weighing perch. British Poultry Science, 26, 229-237. Reuvekamp, B. (1991a). Goede ervaringen met hennen wegen in etagestallen, Pluimveehouderi} 20 September 1991,21: 18-19. Reuvekamp, B. (1991 b) Betrouwbaarheid gegevens automatische dierweegsystemen, Praktijkonderzoek voor de Pluimveehouderi}, 3, 12-14. Ross, c.c. and N. Davis (1990). Sensors in poultry housing, Poultry International, February 1990, 26-30. Schofield, c.P. (1990). Evaluation ofirnage analysis as means of estimating the weight of pigs, Journal of Agricultural Engineering research, 47,287296. Schofield, c.P., lA. Marchant, R.P. White, N. Brandl and M. Wilson (1999). Monitoring Pig Growth using a Prototype Imaging System. Journal ofAgricultural Engineering Research, 72,205-210. Turner, M.lB., P. Gurney and c.G. Belyavin (1983). Automatic weighing of layer-replacement pullets housed on litter or in cages. British Poultry Science, 24, 33-45 Turner, M.lB., P. Gurney, J.S.W Crowther and lR. Sharp (1984). An automatic weighing system for poultry, Journal ofAgricultural Engineering Research, 29, 17-24.
212
BEHAVIOUR OF CIDCKENS TOWARDS AUTOMATIC WEIGIDNG SYSTEMS
A. Cbedad, E. Vranken, J.-M. Aerts, D. Berckmans(l)
(I)
Laboratory for Agricultural Buildings Research, Katholieke Universiteit Leuven, Kardinaal Mercierlaan 92, 3001 Leuven, Belgium Corresponding author, e-mail: [email protected]. te!. +32-16-32. 14. 36,fax +32-16-32.14.80
Abstract: In previous studies it was found that automatic broiler weighing systems deal with some accuracy problems. Researchers reported poor agreement between automatic and manual mean weighing used as a reference. The difference was observed especially after 4 weeks and this was explained by assuming that the weighing system is less visited by heavier chickens at the end of the production period. The objective of the research reported in this paper was to test this hypothesis under practical production conditions. From the image analysis, the higher formulated hypothesis that 'the system was used less frequently by heavier animals' could be confirmed. Copyright © 2000 IFAC Keywords: Image Analysis, Monitoring, Behaviour
1.
tarred, so that faeces and litter are weighed with. After a number of weighing, a new average is calculated and the above and below limit re-adapted. Once calibrated, the system has an automatic tare weight. To prevent two birds being recorded at one time, a number of readings of each individuals birds are averaged and must be within +1- 10 % of each other to be valid (Anonymous, 1982). The implementation of a computerised weighing system has resulted in a substantial reduction in the amount of time (60 to 65%; Feighner, et al., 1984) necessary for data acquisition and analysis. At the end of each run the following information is presented either on the screen or on the printer: date, start time, duration run, total number of weights recorded, mean weight, standard deviation, number recorded in various weight bands or intervals, and a list of the individual weight recordings in order of recording.
INTRODUCTION
It is of major importance to the flock manager to
have on-line information about weight, uniformity and growth of the birds, feed conversion efficiency (Newberry, et al., 1985; Wolterink, and Meijerhof, 1989), occurrence of disease problems and vitality (Meijerhof, 1989), and also to be able to forecast some days in advance the average weight and spread of weights at slaughter (Turner, et al., 1984). An accurate prediction of not only average weight but also the range of weight distribution throughout the flock can be of valuable assistance to the producer planning his killing program and scheduling broiler pick-up and slaughter (Ross, et al., 1990). Determining broilers mean body weight in an on-line way is done today by automatic weighing: 1.2 Automatic weighing
However, automated broiler weighing systems still have some implementation problems that need to be overcome. For example, the design of perch is fairly critical in that the perch must attract high, representative numbers of birds each day but not be so acceptable that birds having mounted it stay for long periods (Turner, et aI., 1981).
Automatic weighing involves introducing one or more portable platform balances into the broiler house that are connected to a data analyses unit. The output from the platform is processed so that the weight of any broiler getting on the perch and staying there for a few seconds is recorded. The computer program used for weight recording provides for automatic recording of the weight of the empty balance so that this can be subtracted from the weight of an occupied balance to arrive at the weight of the broiler. After weighing, the balance is automatically
Some researchers obtained poor agreement between automatic and manual mean weighing. Newberry, et al. (1985) and Blokhuis, et al. (1988) found that the automatic body weights were lower than the manual
207
metallic frame at a distance of 0.9 m above the floor and was connected to a video recorder. The distance to the floor was taken similar in the different experiments in order to be able to compare the images of different experiments. The camera was equipped with a telephoto lens (Cosmicar 4.8 mm 1: 1.16) to allow the desired area of view to be captured. The automatic weighing system was placed on the floor under the CCD camera. The measured data from the weighing system were stored on a Pc. It was possible from this height to observe the animals on a surface of circa 2.3 m x 1.6 m. Stable (1) (CLO) was lighted with commonly used TLlamps (Phillips 36 W) and produced a light intensity of 194 lux which was measured on 6 random places in the stable. This illumination resulted in remarkable quality of the recorded images. Besides the good illumination, the floor was covered with shopped straw, which improves the contrast between the animal and the background. The stable had a dimension of 11.6 m x 16.2 m with a total of 2900 birds at a stocking density of 16 birds/ m 2 (mixed gender, genetic strain: Ross). Four automatic weighing systems were placed between the feederand drink station. The food and water were automatically administered.
means. This difference seems higher in the fifth week compared to week three. Different flocks show somewhat different deviations, varying between 1.5 and 5.5%. It was summarised by Hughes, et al. (1977), that there was a tendency for frequent perchers to be lighter than infrequent ones. The difference was observed especially in week 6 (Blokhuis, et aI., 1988). Blokhuis, et aI., (1988) , explained this by assuming that the weighing system was less visited by heavier birds at the end of the production period. Experience of WoIterink, et aI., (1989), is that such a weighing system works good with broilers at a young age but in the period of the fmishing phase the scales are visited less frequently. Although these systems give indications about the relative path of the growth curve of the flock but the exactness of the measurements seems still doubtful, especially at the end of the growing period (Blokhuis, et aI., 1988). The objective of this paper is to test the above mentioned hypothesis: that the weighing system is less visited by heavier chickens at the end of the production period formulated by Blokhuis, et aI., ( 1988). 2.
MATERIAL AND METHODS
2.1 Determining weight by digital image processing Another alternative is to estimate broiler chicken live weight from its dimensions by means of image analysis techniques. This method was effectively used in the estimation of the bodyweights (Schofield, et aI., 1990; Ali, et al., 1993; Marchant, and Schofield, 1993) and carcass characteristics (Van der Stuyft, 1994) of pigs. Under interactive operator control, the live weight of pigs could be determined within +/- 3 % of the actual weight, if poor pictures were discarded (Schofield, et aI., 1999). Under optimal lighting conditions, De Wet, et al. (1998), developed an imaging system for individual broilers to determine the body weight from its dimension (upper view) in pixels. The weight increases proportional with the surface area (a second-degree function). A high correlation (R = 0.98) was obtained between the surface area pixel value from the upper view and their weight.
Fig.l. Schematic representation of the experimental set-up. Stable (2) (PDLT) was set to an illumination of 5 lux. This illumination was too weak to extract relevant information form the images. An extra light source (Philips 350 W) was mounted at a height of 2.3 m above the ground and produced a light intensity of 174 lux in the neighbourhood of the scale. The stable had a dimension of 15 m x 10 m and was divided into four equal parts with a total of 1500 birds at a stocking density of 20 birds/ m 2 (mixed gender, genetic strain: Ross). In each part, an automatic weighing system was placed between the feeder- and drink station. The imaging system was placed above one of those platforms.
2.2 Experimental set up In order to test the higher formulated hypothesis, images from broilers on and around the scale had to be collected during a production period (42 days). The experiments were performed on 3 locations, namely at the "Provenciale Dienst van Land en Tuinbouw, Antwerpen" (PDLT), "Centrum van Landbouwkundig Onderzoek, Merelbeke" (CLO) and the "Zootechnisch Centrum, Lovenjoel" (ZTC). A CCD "Charged coupled device" monochrome camera (KP-MIEIK HITACHI) was mounted on a
208
In a picture represented by L grey levels [1,2, ... ,L], the pixels at level I is denoted by nj and the total number ofpixels by N = nl +n2 + .. . nL.
Finally, stable (3) (ZTC) was set to an illumination of 199 lux, which produced images from a good quality. The 50 birds were kept on wood shavings and occupied 3 m x 4.3 m. It was possible to keep 16 animals per m 2• The images were stored on VHS video tapes and were digitised on 486DXl40 PC fitted with a frame grabber (Matrox, PIP-I024B) capable of capturing a 256 x 256 pixels image. The imaging system registered 4 to 8 times a day during 10 minutes each. During each measurement period, 16 images were digitised 5 times every 2 minutes and were stored in a file named with the date and time of the measurement so that 80 digitised images were obtained after one measurement period of 10 minutes.
To evaluate the "goodness" of the threshold level k, Otsu introduces the discriminate criterion which is determined as the between-class variance
O"~(k) =aJo(k)[uo(k)- liT ]2 + aJdk)[udk )- liT ]2 [lIraJ(k) - ,u(k)f aJ(k)[l- aJ(k)]
where O"p(k) denotes the between-class variance of the background and object, aJo (k), aJI (k ) : probability of class occurrence of the background and object, respectively, 110 (k ),111 (k): probability of class mean level of the background and object, respectively and IIr (k): the total mean level of the picture.
2.3 Method Correction ofNonunifonn illumination. In some parts of the images the object are brighter than in other parts. Let a(iJ) be an image with iJ E [O,255j.
The optimal threshold k • is given by the maximum argument (Argmax) defmed by
The following procedure is used to fmd a coarse estimate of the background illumination, by determining the minimum of each 32 x 32 block in the image. Let I(iJ) be a 3 dim surface of the nonuniforme illuminated image a (iJ). The values of I(iJ) are calculated by determining the minimum of each 32 x 32 block in the image or: I(i,j)
= Min
a(i,})
i,} E32x32
~p(e) = max ~p(k)
= a(i,}) -
I(i,})
i,} E(0,255]
(4)
l~k~L
The method described has low computational load due to the exploitation of only the zero-and firstorder cumulative moments of the histogram up to the level k. Calculation time can also be reduced by restricting the range of k over which the maximum is sought from the application.
(1)
The coarse estimate is then expanded in size so that it has the same size as the original image. The computed illumination is subtracted from the original image to correct for the nonuniformity or: d(i,})
(3)
Label Algorithm. Once a gray level image has been processed to remove noise and thresholded to produce a binary image, a connected components labeling operator can be employed to group the binary-l pixels into maximal connected regions (Haralick and Shapiro, 1992). Its input is a binary image and its output is a symbolic image in which the label assigned to each pixel is an integer uniquely identifying the connected component to which that pixels belongs. Once the regions where labelled, a binary erosion followed by a dilatation was appllied.
(2)
Unfortunately, these operations also darken the broilers on the image. By rescaling the pixel intensities, the entire intensity range is filled. This makes the broilers brighter, with more visible details. Automatic Thresholding: "Otsu Method". Since there is a continuous change in contrast between the animal and background during the production period (the feather of the broilers are yellow on a yellow like background at the beginning of the production. At the end the feather became white and the background more black like), an automatic threshold method is needed. This method determines an automatic threshold as Ling, et aI., (1996), for measuring the canopy for tomato seedling describes it. The above conditions motivated the selection of an adaptive thresholding technique for the segmentation task. The Otsu method selects an optimal threshold value based on the discriminate criteria that evaluates a "goodness" of the threshold at any level (Otsu, et
Algorithm. The algorithm for calculating the area of the animals that visited the scale and those, who did not, consists of the procedures mentioned above. It consists of the following steps: 1. A file is loaded. Every file consists of 16 digitised images of (256 x 256) pixels. 2. Selection of area around the balance. 3. The nonuniform illumination of the image is calculated and corrected. 4. Calculating of the optimal Threshold "Otsu method". 5. Construction of a binary image.
al.,1979) .
209
The algorithm for calculating the area of the animals that did not visit the scale is similar except that a bigger area around the weighing system is selected (1m2) . A flow chart of the segmentation algorithm is shown in figure 2.
Selection of the area around the weighing system
3. Correction of non uniform illumination
RESULTS AND DISCUSSION
The average values for surface area pixels were determined for broilers that jumped on the scale and those, who did not. In order to perform the surface estimation accurately, it is essential to calculate the average surface over sufficient images from the same animal. For that, 16 images were taken from the same animal. The averages for the surface area and standard deviation for the animals that came up the scale and those, who did not, are represented in table 1. At stable (1), 200 broilers were weekly manual weighed for reference. The weight obtained after day 36 and 42 by means of the manual weighing and automatic weighing was respectively (1892 ± 40; 2124 ± 10) and (2350 ± 34; 2555 ±20). From results obtained, it is clear that the weight measured by the automatic scale is underestimated from day 36. In figure 3, the average surface of the animals that visited the weighing system (full line) and those that were within the selected area (dotted line) around the weighing system is plotted in function of time. The deviation between both lines became clear from approximately day 36 of the production period.
no
Construction of a binary image
Regions in the image are labelled.
no
Calculation of daily mean area en stdv.
Area ofbroil"" that vi sted the balance .. •.. Area of chickens around the balance
3000
' 000 500
Fig. 2. A flowchart of the developed segmentation algorithm. 6. 7. 8.
9.
'0
20
25
30
'5
lime(doys)
Segments or region are labelled. The connected components labelling algorithm is applied. On each segment, binary erosion followed by a dilatation was applied. The area of the regions are calculated. If the value is within an acceptance range, it will be stored on the disk. Areas that are in touch with the boundaries are eliminated. The daily average area and standard deviation are calculated from the captured images.
Fig. 3. Surface area of broilers that visited the weighing system (full line) and those who did not (dotted line). Data from CLO.
210
Table 1. Daily mean area with standard deviation of broilers that visited the scale and those. who did not
Day
15 16 17 18 22 23 24 25 26 29 30 31 32 33 34 36 37 38 41 42
Surface area of broiler (data CLO) (Mean + Stdev) Surface of Surface of broilers on broilers around the the balance balance 607 ± 15 628 ± 50 611, ± 22 611 ± 33 616 ± 36 930 ±9 682 ± 34 619 ± 35 944 ± 45 918 ± 25 946 ± 54 912 ± 22 997 ± 52 978 ± 26 1062 ± 88 1176±61 1077 ± 26 1088 ± 35 1170 ± 48 1034 ± 51 1161 ± 18 1091 ± 41 1432 ± 74 1224 ± 9 1370 ± 56 1401 ± 92 1298 ± 87 1487 ± 96 1518±24 1526±81 1705 ± 8 1680 ± 12 1909 ± 27 1633±15 1868 ± 47 1998 ± 97 2387 ± 05 2110 ± 21 2179 ± 58 2857 ± 91
Surface area of broiler (data PDLT) (Mean + Stdev) Surface of Surface of broilers broilers on around the the balance balance 567 ±4 590 ± 12 623 ± 44 604 ± 51 702 ± 25 685 ± 17 714 ± 34 720 ± 41 920 ± 40 890 ± 72 917±41 933 ± 32 912 ± 29 902 ± 54 1040 ± 51 994 ± 31 1127 ± 62 1056 ± 47 1214±28 1280 ± 43 1264 ± 54 1278 ± 74 1302 ± 16 1231 ± 25 1380 ± 23 1282 ± 11 1467 ± 65 1350 ± 26 1540 ± 14 1459 ± 24 1602 ± 58 1508±89 1695 ± 67 1556 ± 47 1720 ± 25 1621 ± 78 2704 ± 47 2136 ± 84 2965 ± 41 2235 ± 71
ACKNOWLEDGEMENTS
Since the weight increases proportional with the 2D surface area (De Wet, et a/., 1998), it can be concluded that the animals around the balance are heavier than those, which come up the weighing system, which means that the weight measurements are under estimated. This explains the low measurement accuracy of an automatic weighing system. It must be clear that weighing system is accurate (1 gram) but the fact that lighter birds are coming up at the end of the production period, makes this measurement technique inaccurate. From figure 4, it is clear that there was a tendency for frequent percher to be lighter than infrequent ones. The difference is observed especially from day 36 (week 6, table 2) as it was found by Blokhuis, et a/. (1988). It is also clear from table 1 that the weighing system works well for broilers until the age of 36 days, as found in literature by Wolterink, et a/. (1989).
4.
Surface area of broiler (data ZTq (Mean + Stdev) Surface of Surface of broilers on broilers the around the balance balance 520 ± 23 576 ± 51 614 ± 51 602 ± 24 656 ± 74 632 ± 47 670 ± 26 612 ± 12 726 ± 33 701 ± 36 820 ± 12 814 ± 44 852 ± 51 822 ± 15 890 ± 54 862 ± 56 930 ± 41 945 ± 41 944 ± 23 987 ± 26 1077 ± 18 999 ± 58 1023 ± 61 1045 ± 78 1156 ± 24 1103±81 1254 ± 41 1198 ± 63 1347 ± 47 1477 ± 71 1401 ± 61 1516 ± 10 1478 ± 76 1656 ± 15 1521 ± 81 1750 ± 66 2314 ± 67 1888 ± 27 1957 ± 63 2465 ± 57
The authors are grateful to the Ministry of Small Enterprises and Agriculture (Belgium) for fmancial support. The PDLT and CLO provided field trial facilities and assistance in gathering data, and the authors are grateful to Ir. 10han Zoons from PDLT and Ir. Marijke Lippens from CLO for their help and co-operation.
REFERENCES Ali, N.M. (1993) . Variance in pigs dimensions as measured by image analysis. Livestock environment IV, Fourth International Symposium University of Warwick Coventry England, American Society ofAgricultural Engineers, Michigan, 151-158. Anonymous (1982). Live bird weighing simplified, Poultry International, 6:44-50. Blokhuis, HJ., J.W. Van Der Haar, and J.M.M Fuchs (1988). Do weighing figures represent the flock average?, Poultry International, 4(5), 17-19. De Wet, L., E. Vranken and D . Berckmans (1998). Determination of daily broiler bodyweight changes using computer-assisted image analysis. Proceedings of the World Poultry Science Association Conference held on October 16th, 1998 at Pretoria, South Africa, 19 p.
CONCLUSIONS
The hypothesis that "the weighing system is less visited by heavier broilers at the end of the production period" formulated by Blokhuis, et a/. (1988) and W olterink, et al. (1989) was confirmed. The difference was observed from day 36 with an average deviation of 19,4 % between the top vieuw area of broilers that came up the weighing system and those who did not (table 1).
21 1
Van der Stuyft, E. (1994) Exterieurwaarden·ng bi} levende varkens gebaseerd op driedimensionale digitale beeldanalyse, Phd Thesis Nr. 255 at the faculty of Agricultural and Applied Biological Science, K.U.Leuven, 318p.
Doyle, I. and S. Leeson (1989). Automatic weighing of poultry reared on a litter floor, Canadian Journal ofAnimal Science, 69, 1075-1081. Feighner, S.D., E.F. Godowsky and B.M. Miller (1986). Portable microcomputer-based weighing systems: Applications in poultry science, Poultry Science, 65: 868-873. Haralick, R.E. and L.G. Shapiro (1992). Computer and Robot Vision. Addison-Wesley Publishing Company.'New York. Hughes, B.O. and H.A. Elson (1977). The use of perches by broilers in floor pens. British Poultry Science, 18: 715-722. Wolterink, K.H. and R. Meijerhof(1989). Dierweegsysteem geschikt voor opfok, Pluimveehouderi} 6 Januari 1989,19:16-17. Ling, P.P. and V.N. Ruzhitsky, (1996). Machine vision techniques for measuring the canopy of tomato seedlings, Journal of Agricultural Engineering Research, 65, 85-95. Lokhorst, C. (1996). Automatic weighing of individual laying hens in aviary housing systems, British Poultry Science, 37: 485-499. Marchant, J.A. and c.P. Schofield, (1993). Extending the image process algorithm for outlining pigs in scenes. Computers and electronics in agriculture, 8,261-275. Meijerhof, R. (1989). Automatishe dierweegsystemen bieden voordeel, 'Automtical weighing system gives advantages' , Pluimveehouderri} 25 Augustus 1989, 19: 12-13. Newberry, R.C., lR. Hunt and E.E. Garriner (1985). Behaviour of roaster chickens towards an automatic weighing perch. British Poultry Science, 26, 229-237. Reuvekamp, B. (1991a). Goede ervaringen met hennen wegen in etagestallen, Pluimveehouderi} 20 September 1991,21: 18-19. Reuvekamp, B. (1991 b) Betrouwbaarheid gegevens automatische dierweegsystemen, Praktijkonderzoek voor de Pluimveehouderi}, 3, 12-14. Ross, c.c. and N. Davis (1990). Sensors in poultry housing, Poultry International, February 1990, 26-30. Schofield, c.P. (1990). Evaluation ofirnage analysis as means of estimating the weight of pigs, Journal of Agricultural Engineering research, 47,287296. Schofield, c.P., lA. Marchant, R.P. White, N. Brandl and M. Wilson (1999). Monitoring Pig Growth using a Prototype Imaging System. Journal ofAgricultural Engineering Research, 72,205-210. Turner, M.lB., P. Gurney and c.G. Belyavin (1983). Automatic weighing of layer-replacement pullets housed on litter or in cages. British Poultry Science, 24, 33-45 Turner, M.lB., P. Gurney, J.S.W Crowther and lR. Sharp (1984). An automatic weighing system for poultry, Journal ofAgricultural Engineering Research, 29, 17-24.
212