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Automatic broiler temperature measuring by thermal camera
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Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/issn/15375110
Special Issue: Environmental Stressors Research Paper
Automatic broiler temperature measuring by thermal camera Victor Bloch a, Natan Barchilon a,b, Ilan Halachmi a, Shelly Druyan a,b,* a
Precision Livestock Farming (PLF) Laboratory, Institute of Agricultural Engineering, Agricultural Research Organization (ARO) e The Volcani Centre, Rishon Lezion, 7528809, Israel b Institute of Animal Science, Agricultural Research Organization (ARO), The Volcani Center, 68 HaMakkabbim Road, Rishon Le Ziyyon P. O. Box 15159, Israel
article info
Heat stress of broilers in commercial broiler-houses decreases their productivity and thus
Article history:
farm profitability. Climate control systems use sensors measuring the temperature around
Published online xxx
the broilers, which can be different from the actual body temperature of the broilers. In this research, a method estimating the body temperature of an individual broiler was designed
Keywords:
and validated for commercial broiler-houses. The method is based on a low-cost IR camera
Poultry climate control sensor
calibrated in real time. The algorithm for the IR image processing uses lasso regression
Chicken body temperature
prediction model. A prototype using this method was built and tested in a research broiler-
Low-cost infrared camera
house over 21 days (age 14e35 days) for 15 broilers. The predicted body temperature was compared with the actual body temperature measured by temperature loggers implanted in the abdominal cavity. The accuracy was ±0.27 C measuring one broiler every 16 min on average. In an experiment in a commercial broiler house, a discrepancy between the climate control activation and broiler estimated body temperature was shown. © 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Broiler chickens bred for faster growth rate (Zuidhof, Schneider, Carney, Korver, & Robinson, 2014) require greater feed intake and faster metabolism (Druyan, 2010; Druyan, Shinder, Shlosberg, Cahaner, & Yahav, 2009), resulting in raised internal (metabolic) heat production (Sandercock, Hunter, Mitchell, & Hocking, 2006). However, faster growth isn't yet supported by the necessary complementary increased cardiovascular and respiratory systems (Havenstein, Ferket, & Qureshi, 2003). The broilers themselves generate heat
(Sandercock, Mitchell, & MacLeod, 1995) (Cahaner & Daghir, 2008), and a higher growth rate leads to rather low capability to maintain adequate energy balance and body water balance under practical environmental conditions (Yahav, 2009). Zhou and Yamamoto (1997) found that broiler body temperature increased by 3 C (41e44 C), and skin temperature increased by 6 C (37-43 C) when exposed to heat stress (36 C) for 3 h. The level of hyperthermia from which sufficient recovery can occur was found to be between 44 and 44.5 C, above this level, heat stroke was developed (Yahav, Shinder, Tanny, & Cohen, 2005).
* Corresponding author. Precision Livestock Farming (PLF) Laboratory, Institute of Agricultural Engineering, Agricultural Research Organization (ARO) e The Volcani Centre, Rishon Lezion, 7528809, Israel. E-mail address: [email protected] (S. Druyan). https://doi.org/10.1016/j.biosystemseng.2019.08.011 1537-5110/© 2019 IAgrE. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Nomenclature AT BT BTcore BTIR IR RFID CAD
ambient temperatures body temperature body temperature measured by a thermometer for reference body temperature measured by a method including IR camera infrared radio frequency identification computer aided design
Therefore, hot spells (persistent high temperatures above thermoneutral range) negatively affect broilers welfare and can results in economic losses due to the extended time needed to reach targeted marketing weight. Extended growth period leads to poorer feed conversion and lower efficiency of meat production per rearing space (Cahaner & Leenstra, 1992; Yalcin, Settar, Ozkan, & Cahaner, 1997). Moreover, hot conditions depress the yield and quality of broiler meat (Hadad, Halevy, & Cahaner, 2014), and may lead to PSE (pale, soft, exudative) meat (Sandercock, Hunter, Nute, Mitchell, & Hocking, 2001). To a lesser extent, all these problems occur also in turkeys and laying hens. Thermal stress is caused by adverse combinations of inappropriate temperature, relative humidity, and air ventilation in the micro-climate surrounding the broiler. The micro-climate is an important factor for the broilers’ wellbeing (Yahav et al., 2005), and therefore the construction of modern broiler-houses is insulated from the outdoor environment, and the climate is controlled by ventilation, heating and cooling. However, while the average spatial climate in the house may be properly maintained, the climate at the individual bird level can be unsuitable (Yahav, 2000). This is caused by climate sensors which are distributed in the broilerhouse space and provide measurements above the broilers, while the actual micro-environment felt by the broiler and affecting body temperature (BT) can be dramatically different (Naas, Romanini, Neves, do Nascimento, & Vercellino, 2010). The BT is typically measured using a digital thermometer inserted into the cloaca (cloacal temperature) of a bird (Quimby, Olea-Popelka, & Lappin, 2009). This invasive procedure requires the animal to be handled and restrained (Torrao, Hetem, Meyer, & Fick, 2011), which can result in stress-induced hyperthermia (Dallmann, Steinlechner, von Horsten, & Karl, 2006; Giloh, Shinder, & Yahav, 2012) and is impractical for routine heat stress monitoring. For continuous, reliable and accurate measurement of BT, surgically implanted radio-telemetry data loggers (Dawson & Whittow, 2000) and telemetry devices (Lacey, Hamrita, Lacy, Van Wicklen, & Czarick, 2000) are applied under research conditions. However, with the risk of infection after invasive surgery, recovery time for the animal prior to commencing any subsequent data collection, and the fact that the logger gives only retrospective information, those technologies are impractical at the commercial broiler farm level (Iyasere, Edwards, Bateson, Mitchell, & Guy, 2017). The size of boluses that are successfully used in cattle (Bewley, Einstein, Grott, &
Schutz, 2008) and goats (Castro-Costa, Salama, Moll, Aguilo, & Caja, 2015) is not suitable for poultry. Non-invasive IR techniques are widely used for BT estimation for animals (McCafferty, Gallon, & Nord, 2015) (Halachmi, Guarino, Bewley, & Pastell, 2019). Due to the absence of feather insulation around the broiler eye, the temperature of this region is often closest to the BT, when compared to other peripheral regions, and may be useful for detecting stress responses (Edgar, Lowe, Paul, & Nicol, 2011; Halachmi et al., 2019; Shen, Lei, Liu, Haung, & Lin, 2016; Stewart et al., 2007). The correlation between the broiler core body temperature and the outer temperature measured by an expensive IR camera under well controlled research conditions has been determined by (Giloh et al., 2012). However, all these studies used high-quality IR cameras, which are impractical for commercial applications. In this paper, we propose a method for individual noninvasive BT measurement based on low-cost IR cameras, which can be used in commercial broiler-houses. The method includes automatic acquisition of thermal images of individual broilers, defining body region representing the core BT and applying a statistical model predicting the BT based on thermal imaging. We designed a prototype using this method. The prototype is built on a feeder, hence, it provides continuous measurements from voluntary visits of different broilers. We used the prototype to conduct a proof-of-concept experiment in a research broiler-house as well as under commercial conditions. The goals of this research were to: 1. Design a method and a system prototype for individual broiler BT measurement. 2. Validate the method in an experiment under research broiler-house conditions. The hypothesis of the research was that the individual broiler BT can be measured by a system fitted to use in commercial broiler-houses with accuracy of better than 0.3 C (representing 1/10 of the broiler BT range).
2.
Methods and materials
2.1.
System design
A prototype of a broiler BT measuring system was designed and built (Fig. 1). The prototype included a box with a feed tray connected to a feeder line tube, and a hole where the broiler can insert its head at the required orientation parallel to the IR camera's image plane. An RFID antenna was located around the hole. The IR cameras were located inside the cage at an appropriate distance and orientation relative to the animal's head. A thermistor sensor for the camera calibration was located in the field of view of the cameras. Two types of low-cost IR cameras were tested in this experiment: FlirOne and Lepton, both made by FLIR Systems, Inc. (Wilsonville, Oregon, USA). FlirOne has 60 80 pixels resolution, accuracy 3 C, sensitivity 0.1 C and was installed on a mobile phone (Samsung S5 phone) and controlled by an application developed for this study (Android Studio, Google, California). Lepton has 60 80 pixels resolution, accuracy 5 C,
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Fig. 1 e A prototype of the body temperature measuring system (left side) operating at an experimental broiler house (rightside) before implementing in commercial broiler houses. It includes feeder (1), box (2), RFID antenna (3), hole for a broiler (4), thermal camera (5).
sensitivity 0.05 C and was controlled by a microcontroller (Raspberry Pi 3). The temperature drift of both cameras was calibrated by a thermistor sensor PT100 (Din 1/5) coated with black vinyl electrical tape (Brand 88, Scotch™) with emissivity of 0.96, and located 7 cm from the cameras. The calibrating thermistor temperature was measured by a microcontroller (Uno, Arduino). The calibration was performed for each picture. The broilers were recognised by the RFID tags (APT12, BioMark Inc., Idaho, USA), antenna and reader (HPR Plus, BioMark Inc., USA).
2.2.
Validation experiment
2.2.1.
Animal
A total of thirty male broiler chicks of a commercial genotype (Cobb 500 described by (Vantress, 2012)) were placed together on day of hatch at the ARO research experimental farm (Volcani center, ARO). The chicks were placed on wooden shaving litter floor. In this phase, the broiler-house had no climate control (except for heaters for the early age), and temperature of the facility was set at 33 C, according to recommendations for broilers of this age (Vantress, 2012). Commercial feed (22% crude protein, 5.8% ash, 3035 kcal kg1; Y. Brown & Sons Ltd, Hod HaSharon, Israel) and water were provided ad libitum. Lighting conditions during the entire period were 18L:6D. The brooding period was from day 1 to day 14, and at the start of the data collection, the birds were 14 days old with an average mass of 486 ± 7.2 g.
2.2.2.
International, USA) to protect them (Fig. 2a) and then implanted into the abdominal cavity as described below. All 30 broilers were implanted with RFID tags in the upper side of neck (Fig. 2b). The naı¨ve (control group) birds without the temperature loggers were used to produce sufficient heat level similar to the commercial broiler-houses, and as a control group for the natural development of the broilers with loggers implanted. The broilers had free choice to eat either from the prototype feeder or from an additional feeder.
2.2.3.
Data collection
From 14 days of age onward for 21 days, the data about the feeder visiting and broiler temperature were recorded. At every visit to the feeder, the RFID recorded the broiler number, visit time and duration of the head being inside the cage. At each visit, the cameras captured and saved images for 20 s at 2 Hz frequency, totally 40 images. The data from the IR cameras, RFID and the temperature loggers was synchronised during each visit, comparing the core BT (BTcore) monitored by the temperature loggers and BT predicted by a model based on images from the IR cameras (BTIR). Altogether, data from about 1220 meals was collected. The temperature logger sampling time was 10 min.
2.3.
IR image processing
In order to determine the temperature from the IR image, only images with the broiler head parallel to the image plane (Fig. 3a) were selected. The following sequence of image processing operations was performed for each image:
Surgery implanted temperature loggers
All the procedures in this study were carried out in accordance with the accepted ethical and welfare standards of the Israel Ethics Committee (IL-710/17). At day 14 all the birds were weighed, and then 15 birds representative of mean body mass ± 30 g were selected for surgical implantation of temperature logger (SL53T-A, Signatrol Ltd, UK, external diameter 22.5 mm and width 6.5 mm). Prior to implantation, each logger was calibrated to 0.14 C accuracy. The temperature loggers were coated (Plasti Dip
1. The broiler head was extracted by binary image segmentation using threshold of 35 C. 2. The head shape was enclosed by a rectangle. If the ratio between the rectangle height and width was more than 1, the image was discarded assuming that the head orientation was not parallel to the image plane. 3. Groups of hottest pixels in the facial area were extracted to calculate the features of the BT prediction model described below.
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Fig. 2 e The temperature loggers intended to measure reference body temperature in the abdominal cavity. The loggers (a) were covered by protecting material. RFID tags used for timing the logger temperature (b) were adjusted in the upper side of neck.
Fig. 3 e Infrared image (IR) recorded by the FlirOne (upper row) and Lepton (lower row) cameras with the broiler head parallel to the image (a) and non-parallel (b) and (c). Square represents the head area, and dot represents the pixel with the highest temperature (hottest).
All images captured by the camera during each meal were processed as described above. The pictures were taken while broilers were moving or still. It is assumed that if the broiler were moving, the cameras caught a smoothed image, which was equivalent to applying a numerical averaging filter, resulting in a decrease of the maximal temperature value. Nevertheless, if the broiler was still, the image represented its actual face temperature. Hence, among all the images with the needed head location and orientation, the image with the highest value of the hottest pixel was assumed to be the most representative image during that meal.
2.4.
Modelling
To build a regression-based model for predicting BT, features of the IR image were used. The hottest pixel in the face area
and the following pixel groups were used to extract the IR image features: 3, 10, 30, 50, 100, and 300 hottest pixels (the face area typically occupies 300 pixels). The following features were extracted from the groups: average, minimal value and standard deviation. The following additional features were used: thermistor temperature, environment temperature (average of 200 pixels of the system side wall, providing a reference from an environment object), meal maxima average (average of the hottest pixels of all 40 IR images taken during the meal), broiler age (hours). In total, the number of features was 1 þ 6*3 þ 4 ¼ 23. To fit the non-linear factors of the BT estimation, a polynomial model of order three was used, and powers two and three (denoted by superscripts 2 and 3) of the existing features were added to the regression (multiplying the number of features by 3).
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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To prevent overestimation of the regression because of the large number of variables and find the most significant features (with the strongest influence on the R2), lasso regression (Tibshirani, 1996) was applied. The calculations were performed on MATLAB (MathWorks). The R2 and accuracy of the model were calculated by a regression between the reference and predicted BT. The model error and R2 was validated by 10 fold cross validation (Kohavi, 1995). To analyse the model and simplify the calculations, the features with the greatest significance in the model were found. First, 20 features with nonzero coefficients found by the lasso regression (with l ¼ 0.005) were taken. Then, the stepwise regression method (Harrell, 2001) was used to find the six most significant features.
2.5.
Validation under commercial conditions
Based on the laboratory experiment, an updated prototype was developed and installed in a commercial broiler house (30,000 broilers, 14 broilers m2). The system continually recorded facial temperature between days 14e35 and the data were incorporated into the suggested model in order to evaluate BTIR. Chicken house climate control data were also collected and synchronised with BTIR. The upper critical temperature limit during the growing period (week 3e5) varies according to the genetics, age and diet, and is suggested to be within the range of 29e32 C (Esmay, 1982; Hahn, 1982; Manual 2012), while the recommended comfort environmental temperature is approximately 19e23 C (Cobb broiler management guide (Vantress, 2012)), when the broiler body temperature is around 42 C. In the commercial broiler house, the lower critical
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environmental temperature was set to 29 C, and body temperature set-point was set to 41.8 C.
3.
Results
The correlation between the implanted temperature loggers (BTcore) and the lasso regression model based on IR camera (BTIR) are presented in Fig. 4. The regression R2, standard error of estimate, slope, and bias values are presented in Table 1. The P-values were less than 0.001 for the both cameras. The highest correlation (R2 ¼ 0.74) and the lowest error (0.27 C) between BTcore and the BTIR was achieved for Lepton camera. The close values of the cross validation approved the estimation of the method error. The main features for the both cameras are presented in Table 2. The lasso models built for the Lepton camera with the most significant features result in R2 and the standard error of the estimate (presented in the two last rows of Table 2). Other features were insignificant. Three of the most significant features are same for both cameras (thermistor temperature, corrected temperature and (meal maxima average)3). The ambient broiler house temperature and the measured broiler body temperature during day 16 (age 30 days) of the experiment in a commercial broiler house are presented in Fig. 5. According to the broiler house ambient temperature, cooling was deactivated at hour 19, while the real average broiler BT was above the threshold indicating heat stress for additional 2 h and for 3 h for specific broilers. Taking into account the BT measuring error of ±0.27 C, individual broiler had heat stress during 2 h (points above the dash-dotted line).
Fig. 4 e Body temperature measured by implanted temperature loggers in the broiler abdominal cavity (BTcore) is compared with infrared thermography temperature (BTIR) monitored by FlirOne (dots) and Lepton (circles) cameras.
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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In the morning, the climate control was activated according to the ambient temperature at 8:30, an hour and a half after individual broilers indicated heat stress at 7:00.
4.
Discussion
Assuming that the temperature range between a common broiler body temperature around 40 C and heat stress threshold 41.8 C is about 2 C, the achieved accuracy of the method (R2 ¼ 0.74 and standard error of estimate of 0.27 C) is potentially sufficient for further research under different conditions and in practical applications. The selected lasso model and the low-cost IR camera are similar to a previous study. Giloh et al. (2012) reached a better correlation (R2 ¼ 0.82 vs. 0.74), but used a high-cost, highquality IR camera (FLIR PM545) and black body in the poultry house. Furthermore, they manually held each bird with eyebeak line facing the thermal camera, and used manual operated rectal temperature measured by a digital thermometer as the reference. This invasive procedure is impractical under commercial conditions and might artificially influence the bird body temperature (Edgar, Nicol, Pugh, & Paul, 2013). Between the most significant features of the lasso regression models for both cameras are STD of 50 and 300 hottest pixels in the image (Table 2). One possible explanation for the influence of the STD on the BT estimation is difference between the hottest areas (eyes) characterising the BT and insulated feather area with the temperature independent of the BT. In addition, the model includes environment temperature and objects on the background. Excluding them from the model decreases the R2 to 0.62. This shows the importance of the camera calibration, which should be improved in future prototypes. The significance of “meal maxima average”
feature suggests that all the pictures taken during the meal should be considered for the feature extraction. The accuracy of the model achieved in this study was influenced by a number of factors: the accuracy of the equipment (thermal cameras, thermistor and temperature loggers), accuracy of the algorithm extracting features from all the thermal images achieved during a meal, and diversity of the model for different birds. Considering the practical case, when birds in commercial farms cannot be identified, we assumed similarity of the model for different birds, and therefore, did not use more complicated models taking into account personal animal features (such as mixed model). A principal advantage of the presented method is measuring BT without human intervention. During the validation experiment, 15 broilers voluntarily visited the system prototype, each for 16 min on average. In the commercial broiler house experiment, the body temperature was measured once in about 3 min on average due to higher animal density. Since the developed system measured the body temperature of individual broilers, the criteria for the climate control activation could be changed. For example, the climate control might be activated not according to the average body temperature of all birds, but according to the percentage of individuals under heat stress. By our experience, 10% of broilers under thermal stress can represent a good threshold for activating a climate control. The chosen camera and developed statistical model will be applied for experiments in a commercial broiler-house. The following questions as yet to be studied: fitting the system design to varied and tough farm conditions, finding the number of systems needed for an entire broiler-house, and the full automated integration into the farm climate control loop.
Table 1 e Validation of the lasso regression for FlirOne and Lepton cameras. Correlation (R2) and Standard error of the estimate (SEE) are presented for the model and cross validation (CV). Camera
[R2]
CV [R2, mean ± STD]
SEE [ºC]
CV SEE [ºC, mean ± STD]
Slope
Bias [ºC]
Flir One Lepton
0.70 0.74
0.699 ± 0.057 0.732 ± 0.044
0.29 0.27
0.301 ± 0.016 0.283 ± 0.016
1.006 1.01
0.26 0.41
Table 2 e The main features used for the lasso model and their coefficients. Feature Standard deviation (STD) of 50 hottest pixels in the image STD of 300 hottest pixels in the image Thermistor temperature Environment temperature Meal maxima average (Minimum within 100 hottest pixels)2 (Mean of 300 hottest pixels in the image)3 (Thermistor temperature)3 (Meal maxima average)3 R2 for lasso with the main features Standard error of the estimate for lasso with the main features [ºC]
Coef. Flir One
Coef. Lepton
1.22 0.33 0.06 1.54
0.55 0.16 0.05 0.00034
0.0001 0.00033 0.63 0.32
0.00015 0.00019 0.72 0.28
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Fig. 5 e Broiler body temperature (upper graph) based on infrared thermal camera and average ambient temperature (lower graph) in a commercial broiler house. Lines represent average values, dots represent temperature of individual broilers. Hour 0 represents the midnight.
5.
Conclusions
In this study, a system and method for monitoring broiler body temperature was designed and validated. The temperature was measured automatically and constantly without human interference. The system used a relatively low-cost camera and was designed to be installed in commercial broiler houses. Results show that this system might be effectively used as a temperature sensor in the climate control loop in chicken house.
Acknowledgment This study was support by grants from the Israeli Chief Scientist of Agriculture, project 459-451415 (“Kendel").
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Sandercock, D. A., Hunter, R. R., Nute, G. R., Mitchell, M. A., & Hocking, P. M. (2001). Acute heat stress-induced alterations in blood acid-base status and skeletal muscle membrane integrity in broiler chickens at two ages: Implications for meat quality. Poultry Science, 80, 418e425. https://doi.org/10.1093/ps/ 80.4.418. Sandercock, D. A., Mitchell, M. A., & MacLeod, M. G. (1995). Metabolic heat production in fast and slow growing broiler chickens during acute heat stress. British Poultry Science, 57(1), 134e141. https://doi.org/10.1080/00071668.2015.1124067. Shen, P.-N., Lei, P.-K., Liu, Y.-C., Haung, Y.-J., & Lin, J.-L. (2016). Development of a temperature measurement system for a broiler flock with thermal imaging. Engineering in Agriculture Environment and Food, 9(3), 291e295. https://doi.org/10.1016/j. eaef.2016.03.001. Stewart, M., Webster, J. R., Verkerk, G. A., Schaefer, A. L., Colyn, J. J., & Stafford, K. J. (2007). Non-invasive measurement of stress in dairy cows using infrared thermography. Physiology and Behavior, 92(3), 520e525. https://doi.org/10.1016/ j.physbeh.2007.04.034. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58(1), 267e288. Torrao, N. A., Hetem, R. S., Meyer, L. C. R., & Fick, L. G. (2011). Assessment of the use of temperature-sensitive microchips to determine core body temperature in goats. The Veterinary Record, 168, 328e345. https://doi.org/10.1136/vr.c6200. Vantress, C. (2012). Cobb broiler management guide. USA: CobbVantress, Siloam Springs, AR. Yahav, S. (2000). Domestic fowl - strategies to confront environmental conditions. Avian and Poultry Biology Reviews, 11(22), 81e95. Yahav, S. (2009). Alleviating heat stress in domestic fowl different strategies. World's Poultry Science Journal, 65(4), 719e732. https://doi.org/10.1017/S004393390900049X. Yahav, S., Shinder, D., Tanny, J., & Cohen, S. (2005). Sensible heat loss: The broiler's paradox. World's Poultry Science Journal, 61(3), 419e434. https://doi.org/10.1079/WPS200453. Yalcin, S., Settar, P., Ozkan, S., & Cahaner, A. (1997). Comparative evaluation of three commercial broiler stocks in hot versus temperate climates. Poultry Science, 76(7), 921e929. https://doi. org/10.1093/ps/76.7.921. Zhou, W. T., & Yamamoto, S. (1997). Effects of environmental temperature and heat production due to food intake on abdominal temperature, shank skin temperature and respiration rate of broilers. British Poultry Science, 38(1), 107e114. https://doi.org/10.1080/00071669708417949. Zuidhof, M. J., Schneider, B. L., Carney, V. L., Korver, D. R., & Robinson, F. E. (2014). Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005. Poultry Science, 93(12), 2970e2982. https://doi.org/10.3382/ps.2014-04291.
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/issn/15375110
Special Issue: Environmental Stressors Research Paper
Automatic broiler temperature measuring by thermal camera Victor Bloch a, Natan Barchilon a,b, Ilan Halachmi a, Shelly Druyan a,b,* a
Precision Livestock Farming (PLF) Laboratory, Institute of Agricultural Engineering, Agricultural Research Organization (ARO) e The Volcani Centre, Rishon Lezion, 7528809, Israel b Institute of Animal Science, Agricultural Research Organization (ARO), The Volcani Center, 68 HaMakkabbim Road, Rishon Le Ziyyon P. O. Box 15159, Israel
article info
Heat stress of broilers in commercial broiler-houses decreases their productivity and thus
Article history:
farm profitability. Climate control systems use sensors measuring the temperature around
Published online xxx
the broilers, which can be different from the actual body temperature of the broilers. In this research, a method estimating the body temperature of an individual broiler was designed
Keywords:
and validated for commercial broiler-houses. The method is based on a low-cost IR camera
Poultry climate control sensor
calibrated in real time. The algorithm for the IR image processing uses lasso regression
Chicken body temperature
prediction model. A prototype using this method was built and tested in a research broiler-
Low-cost infrared camera
house over 21 days (age 14e35 days) for 15 broilers. The predicted body temperature was compared with the actual body temperature measured by temperature loggers implanted in the abdominal cavity. The accuracy was ±0.27 C measuring one broiler every 16 min on average. In an experiment in a commercial broiler house, a discrepancy between the climate control activation and broiler estimated body temperature was shown. © 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Broiler chickens bred for faster growth rate (Zuidhof, Schneider, Carney, Korver, & Robinson, 2014) require greater feed intake and faster metabolism (Druyan, 2010; Druyan, Shinder, Shlosberg, Cahaner, & Yahav, 2009), resulting in raised internal (metabolic) heat production (Sandercock, Hunter, Mitchell, & Hocking, 2006). However, faster growth isn't yet supported by the necessary complementary increased cardiovascular and respiratory systems (Havenstein, Ferket, & Qureshi, 2003). The broilers themselves generate heat
(Sandercock, Mitchell, & MacLeod, 1995) (Cahaner & Daghir, 2008), and a higher growth rate leads to rather low capability to maintain adequate energy balance and body water balance under practical environmental conditions (Yahav, 2009). Zhou and Yamamoto (1997) found that broiler body temperature increased by 3 C (41e44 C), and skin temperature increased by 6 C (37-43 C) when exposed to heat stress (36 C) for 3 h. The level of hyperthermia from which sufficient recovery can occur was found to be between 44 and 44.5 C, above this level, heat stroke was developed (Yahav, Shinder, Tanny, & Cohen, 2005).
* Corresponding author. Precision Livestock Farming (PLF) Laboratory, Institute of Agricultural Engineering, Agricultural Research Organization (ARO) e The Volcani Centre, Rishon Lezion, 7528809, Israel. E-mail address: [email protected] (S. Druyan). https://doi.org/10.1016/j.biosystemseng.2019.08.011 1537-5110/© 2019 IAgrE. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Nomenclature AT BT BTcore BTIR IR RFID CAD
ambient temperatures body temperature body temperature measured by a thermometer for reference body temperature measured by a method including IR camera infrared radio frequency identification computer aided design
Therefore, hot spells (persistent high temperatures above thermoneutral range) negatively affect broilers welfare and can results in economic losses due to the extended time needed to reach targeted marketing weight. Extended growth period leads to poorer feed conversion and lower efficiency of meat production per rearing space (Cahaner & Leenstra, 1992; Yalcin, Settar, Ozkan, & Cahaner, 1997). Moreover, hot conditions depress the yield and quality of broiler meat (Hadad, Halevy, & Cahaner, 2014), and may lead to PSE (pale, soft, exudative) meat (Sandercock, Hunter, Nute, Mitchell, & Hocking, 2001). To a lesser extent, all these problems occur also in turkeys and laying hens. Thermal stress is caused by adverse combinations of inappropriate temperature, relative humidity, and air ventilation in the micro-climate surrounding the broiler. The micro-climate is an important factor for the broilers’ wellbeing (Yahav et al., 2005), and therefore the construction of modern broiler-houses is insulated from the outdoor environment, and the climate is controlled by ventilation, heating and cooling. However, while the average spatial climate in the house may be properly maintained, the climate at the individual bird level can be unsuitable (Yahav, 2000). This is caused by climate sensors which are distributed in the broilerhouse space and provide measurements above the broilers, while the actual micro-environment felt by the broiler and affecting body temperature (BT) can be dramatically different (Naas, Romanini, Neves, do Nascimento, & Vercellino, 2010). The BT is typically measured using a digital thermometer inserted into the cloaca (cloacal temperature) of a bird (Quimby, Olea-Popelka, & Lappin, 2009). This invasive procedure requires the animal to be handled and restrained (Torrao, Hetem, Meyer, & Fick, 2011), which can result in stress-induced hyperthermia (Dallmann, Steinlechner, von Horsten, & Karl, 2006; Giloh, Shinder, & Yahav, 2012) and is impractical for routine heat stress monitoring. For continuous, reliable and accurate measurement of BT, surgically implanted radio-telemetry data loggers (Dawson & Whittow, 2000) and telemetry devices (Lacey, Hamrita, Lacy, Van Wicklen, & Czarick, 2000) are applied under research conditions. However, with the risk of infection after invasive surgery, recovery time for the animal prior to commencing any subsequent data collection, and the fact that the logger gives only retrospective information, those technologies are impractical at the commercial broiler farm level (Iyasere, Edwards, Bateson, Mitchell, & Guy, 2017). The size of boluses that are successfully used in cattle (Bewley, Einstein, Grott, &
Schutz, 2008) and goats (Castro-Costa, Salama, Moll, Aguilo, & Caja, 2015) is not suitable for poultry. Non-invasive IR techniques are widely used for BT estimation for animals (McCafferty, Gallon, & Nord, 2015) (Halachmi, Guarino, Bewley, & Pastell, 2019). Due to the absence of feather insulation around the broiler eye, the temperature of this region is often closest to the BT, when compared to other peripheral regions, and may be useful for detecting stress responses (Edgar, Lowe, Paul, & Nicol, 2011; Halachmi et al., 2019; Shen, Lei, Liu, Haung, & Lin, 2016; Stewart et al., 2007). The correlation between the broiler core body temperature and the outer temperature measured by an expensive IR camera under well controlled research conditions has been determined by (Giloh et al., 2012). However, all these studies used high-quality IR cameras, which are impractical for commercial applications. In this paper, we propose a method for individual noninvasive BT measurement based on low-cost IR cameras, which can be used in commercial broiler-houses. The method includes automatic acquisition of thermal images of individual broilers, defining body region representing the core BT and applying a statistical model predicting the BT based on thermal imaging. We designed a prototype using this method. The prototype is built on a feeder, hence, it provides continuous measurements from voluntary visits of different broilers. We used the prototype to conduct a proof-of-concept experiment in a research broiler-house as well as under commercial conditions. The goals of this research were to: 1. Design a method and a system prototype for individual broiler BT measurement. 2. Validate the method in an experiment under research broiler-house conditions. The hypothesis of the research was that the individual broiler BT can be measured by a system fitted to use in commercial broiler-houses with accuracy of better than 0.3 C (representing 1/10 of the broiler BT range).
2.
Methods and materials
2.1.
System design
A prototype of a broiler BT measuring system was designed and built (Fig. 1). The prototype included a box with a feed tray connected to a feeder line tube, and a hole where the broiler can insert its head at the required orientation parallel to the IR camera's image plane. An RFID antenna was located around the hole. The IR cameras were located inside the cage at an appropriate distance and orientation relative to the animal's head. A thermistor sensor for the camera calibration was located in the field of view of the cameras. Two types of low-cost IR cameras were tested in this experiment: FlirOne and Lepton, both made by FLIR Systems, Inc. (Wilsonville, Oregon, USA). FlirOne has 60 80 pixels resolution, accuracy 3 C, sensitivity 0.1 C and was installed on a mobile phone (Samsung S5 phone) and controlled by an application developed for this study (Android Studio, Google, California). Lepton has 60 80 pixels resolution, accuracy 5 C,
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Fig. 1 e A prototype of the body temperature measuring system (left side) operating at an experimental broiler house (rightside) before implementing in commercial broiler houses. It includes feeder (1), box (2), RFID antenna (3), hole for a broiler (4), thermal camera (5).
sensitivity 0.05 C and was controlled by a microcontroller (Raspberry Pi 3). The temperature drift of both cameras was calibrated by a thermistor sensor PT100 (Din 1/5) coated with black vinyl electrical tape (Brand 88, Scotch™) with emissivity of 0.96, and located 7 cm from the cameras. The calibrating thermistor temperature was measured by a microcontroller (Uno, Arduino). The calibration was performed for each picture. The broilers were recognised by the RFID tags (APT12, BioMark Inc., Idaho, USA), antenna and reader (HPR Plus, BioMark Inc., USA).
2.2.
Validation experiment
2.2.1.
Animal
A total of thirty male broiler chicks of a commercial genotype (Cobb 500 described by (Vantress, 2012)) were placed together on day of hatch at the ARO research experimental farm (Volcani center, ARO). The chicks were placed on wooden shaving litter floor. In this phase, the broiler-house had no climate control (except for heaters for the early age), and temperature of the facility was set at 33 C, according to recommendations for broilers of this age (Vantress, 2012). Commercial feed (22% crude protein, 5.8% ash, 3035 kcal kg1; Y. Brown & Sons Ltd, Hod HaSharon, Israel) and water were provided ad libitum. Lighting conditions during the entire period were 18L:6D. The brooding period was from day 1 to day 14, and at the start of the data collection, the birds were 14 days old with an average mass of 486 ± 7.2 g.
2.2.2.
International, USA) to protect them (Fig. 2a) and then implanted into the abdominal cavity as described below. All 30 broilers were implanted with RFID tags in the upper side of neck (Fig. 2b). The naı¨ve (control group) birds without the temperature loggers were used to produce sufficient heat level similar to the commercial broiler-houses, and as a control group for the natural development of the broilers with loggers implanted. The broilers had free choice to eat either from the prototype feeder or from an additional feeder.
2.2.3.
Data collection
From 14 days of age onward for 21 days, the data about the feeder visiting and broiler temperature were recorded. At every visit to the feeder, the RFID recorded the broiler number, visit time and duration of the head being inside the cage. At each visit, the cameras captured and saved images for 20 s at 2 Hz frequency, totally 40 images. The data from the IR cameras, RFID and the temperature loggers was synchronised during each visit, comparing the core BT (BTcore) monitored by the temperature loggers and BT predicted by a model based on images from the IR cameras (BTIR). Altogether, data from about 1220 meals was collected. The temperature logger sampling time was 10 min.
2.3.
IR image processing
In order to determine the temperature from the IR image, only images with the broiler head parallel to the image plane (Fig. 3a) were selected. The following sequence of image processing operations was performed for each image:
Surgery implanted temperature loggers
All the procedures in this study were carried out in accordance with the accepted ethical and welfare standards of the Israel Ethics Committee (IL-710/17). At day 14 all the birds were weighed, and then 15 birds representative of mean body mass ± 30 g were selected for surgical implantation of temperature logger (SL53T-A, Signatrol Ltd, UK, external diameter 22.5 mm and width 6.5 mm). Prior to implantation, each logger was calibrated to 0.14 C accuracy. The temperature loggers were coated (Plasti Dip
1. The broiler head was extracted by binary image segmentation using threshold of 35 C. 2. The head shape was enclosed by a rectangle. If the ratio between the rectangle height and width was more than 1, the image was discarded assuming that the head orientation was not parallel to the image plane. 3. Groups of hottest pixels in the facial area were extracted to calculate the features of the BT prediction model described below.
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Fig. 2 e The temperature loggers intended to measure reference body temperature in the abdominal cavity. The loggers (a) were covered by protecting material. RFID tags used for timing the logger temperature (b) were adjusted in the upper side of neck.
Fig. 3 e Infrared image (IR) recorded by the FlirOne (upper row) and Lepton (lower row) cameras with the broiler head parallel to the image (a) and non-parallel (b) and (c). Square represents the head area, and dot represents the pixel with the highest temperature (hottest).
All images captured by the camera during each meal were processed as described above. The pictures were taken while broilers were moving or still. It is assumed that if the broiler were moving, the cameras caught a smoothed image, which was equivalent to applying a numerical averaging filter, resulting in a decrease of the maximal temperature value. Nevertheless, if the broiler was still, the image represented its actual face temperature. Hence, among all the images with the needed head location and orientation, the image with the highest value of the hottest pixel was assumed to be the most representative image during that meal.
2.4.
Modelling
To build a regression-based model for predicting BT, features of the IR image were used. The hottest pixel in the face area
and the following pixel groups were used to extract the IR image features: 3, 10, 30, 50, 100, and 300 hottest pixels (the face area typically occupies 300 pixels). The following features were extracted from the groups: average, minimal value and standard deviation. The following additional features were used: thermistor temperature, environment temperature (average of 200 pixels of the system side wall, providing a reference from an environment object), meal maxima average (average of the hottest pixels of all 40 IR images taken during the meal), broiler age (hours). In total, the number of features was 1 þ 6*3 þ 4 ¼ 23. To fit the non-linear factors of the BT estimation, a polynomial model of order three was used, and powers two and three (denoted by superscripts 2 and 3) of the existing features were added to the regression (multiplying the number of features by 3).
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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To prevent overestimation of the regression because of the large number of variables and find the most significant features (with the strongest influence on the R2), lasso regression (Tibshirani, 1996) was applied. The calculations were performed on MATLAB (MathWorks). The R2 and accuracy of the model were calculated by a regression between the reference and predicted BT. The model error and R2 was validated by 10 fold cross validation (Kohavi, 1995). To analyse the model and simplify the calculations, the features with the greatest significance in the model were found. First, 20 features with nonzero coefficients found by the lasso regression (with l ¼ 0.005) were taken. Then, the stepwise regression method (Harrell, 2001) was used to find the six most significant features.
2.5.
Validation under commercial conditions
Based on the laboratory experiment, an updated prototype was developed and installed in a commercial broiler house (30,000 broilers, 14 broilers m2). The system continually recorded facial temperature between days 14e35 and the data were incorporated into the suggested model in order to evaluate BTIR. Chicken house climate control data were also collected and synchronised with BTIR. The upper critical temperature limit during the growing period (week 3e5) varies according to the genetics, age and diet, and is suggested to be within the range of 29e32 C (Esmay, 1982; Hahn, 1982; Manual 2012), while the recommended comfort environmental temperature is approximately 19e23 C (Cobb broiler management guide (Vantress, 2012)), when the broiler body temperature is around 42 C. In the commercial broiler house, the lower critical
5
environmental temperature was set to 29 C, and body temperature set-point was set to 41.8 C.
3.
Results
The correlation between the implanted temperature loggers (BTcore) and the lasso regression model based on IR camera (BTIR) are presented in Fig. 4. The regression R2, standard error of estimate, slope, and bias values are presented in Table 1. The P-values were less than 0.001 for the both cameras. The highest correlation (R2 ¼ 0.74) and the lowest error (0.27 C) between BTcore and the BTIR was achieved for Lepton camera. The close values of the cross validation approved the estimation of the method error. The main features for the both cameras are presented in Table 2. The lasso models built for the Lepton camera with the most significant features result in R2 and the standard error of the estimate (presented in the two last rows of Table 2). Other features were insignificant. Three of the most significant features are same for both cameras (thermistor temperature, corrected temperature and (meal maxima average)3). The ambient broiler house temperature and the measured broiler body temperature during day 16 (age 30 days) of the experiment in a commercial broiler house are presented in Fig. 5. According to the broiler house ambient temperature, cooling was deactivated at hour 19, while the real average broiler BT was above the threshold indicating heat stress for additional 2 h and for 3 h for specific broilers. Taking into account the BT measuring error of ±0.27 C, individual broiler had heat stress during 2 h (points above the dash-dotted line).
Fig. 4 e Body temperature measured by implanted temperature loggers in the broiler abdominal cavity (BTcore) is compared with infrared thermography temperature (BTIR) monitored by FlirOne (dots) and Lepton (circles) cameras.
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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In the morning, the climate control was activated according to the ambient temperature at 8:30, an hour and a half after individual broilers indicated heat stress at 7:00.
4.
Discussion
Assuming that the temperature range between a common broiler body temperature around 40 C and heat stress threshold 41.8 C is about 2 C, the achieved accuracy of the method (R2 ¼ 0.74 and standard error of estimate of 0.27 C) is potentially sufficient for further research under different conditions and in practical applications. The selected lasso model and the low-cost IR camera are similar to a previous study. Giloh et al. (2012) reached a better correlation (R2 ¼ 0.82 vs. 0.74), but used a high-cost, highquality IR camera (FLIR PM545) and black body in the poultry house. Furthermore, they manually held each bird with eyebeak line facing the thermal camera, and used manual operated rectal temperature measured by a digital thermometer as the reference. This invasive procedure is impractical under commercial conditions and might artificially influence the bird body temperature (Edgar, Nicol, Pugh, & Paul, 2013). Between the most significant features of the lasso regression models for both cameras are STD of 50 and 300 hottest pixels in the image (Table 2). One possible explanation for the influence of the STD on the BT estimation is difference between the hottest areas (eyes) characterising the BT and insulated feather area with the temperature independent of the BT. In addition, the model includes environment temperature and objects on the background. Excluding them from the model decreases the R2 to 0.62. This shows the importance of the camera calibration, which should be improved in future prototypes. The significance of “meal maxima average”
feature suggests that all the pictures taken during the meal should be considered for the feature extraction. The accuracy of the model achieved in this study was influenced by a number of factors: the accuracy of the equipment (thermal cameras, thermistor and temperature loggers), accuracy of the algorithm extracting features from all the thermal images achieved during a meal, and diversity of the model for different birds. Considering the practical case, when birds in commercial farms cannot be identified, we assumed similarity of the model for different birds, and therefore, did not use more complicated models taking into account personal animal features (such as mixed model). A principal advantage of the presented method is measuring BT without human intervention. During the validation experiment, 15 broilers voluntarily visited the system prototype, each for 16 min on average. In the commercial broiler house experiment, the body temperature was measured once in about 3 min on average due to higher animal density. Since the developed system measured the body temperature of individual broilers, the criteria for the climate control activation could be changed. For example, the climate control might be activated not according to the average body temperature of all birds, but according to the percentage of individuals under heat stress. By our experience, 10% of broilers under thermal stress can represent a good threshold for activating a climate control. The chosen camera and developed statistical model will be applied for experiments in a commercial broiler-house. The following questions as yet to be studied: fitting the system design to varied and tough farm conditions, finding the number of systems needed for an entire broiler-house, and the full automated integration into the farm climate control loop.
Table 1 e Validation of the lasso regression for FlirOne and Lepton cameras. Correlation (R2) and Standard error of the estimate (SEE) are presented for the model and cross validation (CV). Camera
[R2]
CV [R2, mean ± STD]
SEE [ºC]
CV SEE [ºC, mean ± STD]
Slope
Bias [ºC]
Flir One Lepton
0.70 0.74
0.699 ± 0.057 0.732 ± 0.044
0.29 0.27
0.301 ± 0.016 0.283 ± 0.016
1.006 1.01
0.26 0.41
Table 2 e The main features used for the lasso model and their coefficients. Feature Standard deviation (STD) of 50 hottest pixels in the image STD of 300 hottest pixels in the image Thermistor temperature Environment temperature Meal maxima average (Minimum within 100 hottest pixels)2 (Mean of 300 hottest pixels in the image)3 (Thermistor temperature)3 (Meal maxima average)3 R2 for lasso with the main features Standard error of the estimate for lasso with the main features [ºC]
Coef. Flir One
Coef. Lepton
1.22 0.33 0.06 1.54
0.55 0.16 0.05 0.00034
0.0001 0.00033 0.63 0.32
0.00015 0.00019 0.72 0.28
Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011
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Fig. 5 e Broiler body temperature (upper graph) based on infrared thermal camera and average ambient temperature (lower graph) in a commercial broiler house. Lines represent average values, dots represent temperature of individual broilers. Hour 0 represents the midnight.
5.
Conclusions
In this study, a system and method for monitoring broiler body temperature was designed and validated. The temperature was measured automatically and constantly without human interference. The system used a relatively low-cost camera and was designed to be installed in commercial broiler houses. Results show that this system might be effectively used as a temperature sensor in the climate control loop in chicken house.
Acknowledgment This study was support by grants from the Israeli Chief Scientist of Agriculture, project 459-451415 (“Kendel").
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Please cite this article as: Bloch, V et al., Automatic broiler temperature measuring by thermal camera, Biosystems Engineering, https:// doi.org/10.1016/j.biosystemseng.2019.08.011