Growth rate detection

Monitoring feed conversion, growth rate and BW is important to improve the efficiency and economic benefits of broiler farmers. Considering the relationship between pecking behaviour and eating, 90% of the feed intake of one broiler was correctly detected with pecking sounds in an experimental pen (Aydin et al., Reference Aydin, Bahr, Viazzi, Exadaktylos, Buyse and Berckmans2014). In another study, 86% of the feed intake of 10 broilers was detected accurately (Aydin et al., Reference Aydin, Bahr and Berckmans2015). Subsequently, short-term feeding behaviours of 10 broilers by pecking sound analysis were estimated in real time, and the accuracy of meal size reached 90%, meal duration reached 95%, the numbers of meals per day reached 84% and feeding rate reached 89% (Aydin and Berckmans, Reference Aydin and Berckmans2016). The above three papers were studied in an experimental pen of at most 10 chickens. However, the application to a commercial farm needs to be validated, including filtering out environmental noise and feasibility of individual detection with large bird numbers.

Broiler vocalisations are highly correlated with BW, and sound analysis could be a reliable method of measuring the growth rate of broiler chickens. In an intensive broiler farm, a study showed that with the growth of broiler chickens, the peak frequency of the sounds emitted by broilers decreased clearly during the entire life of the birds (Fontana et al., Reference Fontana, Tullo, Butterworth and Guarino2015 and Reference Fontana, Tullo, Scrase and Butterworth2016). Furthermore, a model to predict the weight of broilers was built and validated by the peak frequency analysis of the broilers’ vocalisations (Fontana et al., Reference Fontana, Tullo, Carpentier, Berckmans, Butterworth, Vranken, Norton, Berckmans and Guarino2017). Vocalisations could thus be used to assist in the automatic monitoring of the growth of broiler chickens.

Disease detection

There is a difference between the sound of healthy and unhealthy chickens. Analyses of the frequency domain features of 15 infected Clostridium perfringens type A chickens and 15 healthy chickens, classified by a supervised learning neural network, showed a classification accuracy of 66.6% 2 days after infection and reached 100% accuracy 8 days after infection (Sadeghi et al., Reference Sadeghi, Banakar, Khazaee and Soleimani2015). Using data-mining methods and Dempster–Shafer evidence theory, a device was made to detect whether a chicken was infected with Newcastle disease, bronchitis virus, avian influenza or healthy (Banakar et al., Reference Banakar, Sadeghi and Shushtari2016). The device could determine the type of disease in each chicken without surrounding noises.

Others

Sound analysis could identify the sex of 1-day-old chickens as well as different genetic strains (Cobb and Ross chickens) (Pereira et al., Reference Pereira, Naas and Garcia2015). Analysing four acoustic sound characteristics showed that the second spectral peak of the sound spectrum could identify the sex of chicks, and both the second spectral peak and the pitch could identify the genetic strain. Stress, such as temperature, fear and physical stress, can affect the health and production of commercial chickens. The sounds of chickens under heat or fear stress were detected in a commercial farm (Lee et al., Reference Lee, Noh, Jang, Park, Chung and Chang2015). The proposed system was able to classify different stresses by support vector machine (SVM) techniques automatically and non-invasively, and both the stress detection accuracy and the average classification accuracy were greater than 96%. The vocal variations of hens anticipating different types of rewards were investigated and showed that different sound cues made different vocal parameters produced by hens (McGrath et al., Reference McGrath, Dunlop, Dwyer, Burman and Phillips2017). It was possible to use the vocal communication of hens as indicators of welfare (e.g., health, stress, growth).

In summary, sound analysis is able to be used as an early warning system by interpreting the behaviours of chickens, predicting health conditions and detecting growth rates. The group sound analysis, which was tested commercially, can isolate the sounds of each bird, but it is impossible to find the exact bird which makes the sound. The sound collection and analysis of individual chicken, which can distinguish the chicken, is still at experimental stage due to the difficulty of collecting each individual sound in a commercial farm. Like voiceprint recognition, if the voice feature model of each bird could be established, the group sound collection can also match chickens to their sounds. Meanwhile, environmental noise is also a big challenge. With the maturation of voice recognition technology and the rapid development of artificial intelligence (AI), animal sound analysis has great potential as an online detection method to improve health and welfare in the poultry industry.

Detecting nest use

Put RFID reader into nest and worn RFID tags on the leg of hens can detect a hen in and out of a nest. A smart nest box was designed to detect the laying behaviour of individual hens (Chien and Chen, Reference Chien and Chen2018). A low-frequency RFID system was used to detect whether hens were in the nest. An egg detection sensor could detect whether hens laid eggs and then weigh the eggs. Design-based Internet of Things enabled all data to be recorded simultaneously in the cloud database and a local SD card at the same time. Assigning each egg to an individual can achieve product traceability and analyse the laying performance of individuals. The system had the potential to monitor the underperforming hens in egg production due to welfare problems.

Establishing hens’ visits to the nest can improve the nest design. Using the Gantner Pigeon System GmbH (Schruns, Austria) RFID system, researchers found that the nest choice was more affected by the nest position, nest occupancy and age of hens than by the appearance of nest curtains (Ringgenberg et al., Reference Ringgenberg, Frohlich, Harlander-Matauschek, Toscano, Wurbel and Roth2015). Detecting laying behaviour could also examine the relationship between laying eggs and keel bone deviations and fractures (Gebhardt-Henrich and Frohlich, Reference Gebhardt-Henrich and Frohlich2015).

Tracking movement and feeding behaviour of individuals

Radio-frequency identification systems can track the position of individual birds to detect the movement and duration at a particular location. With a low-frequency RFID system, an environmental preference chamber with four compartments and two tiers was designed to study the movement in different compartments of four individual birds (Sales et al., Reference Sales, Green, Gates, Brown-Brandl and Eigenberg2015). Compared with video observation, successful detection rates were 62.6 ± 11.2%. The detection range of the antenna, the scan interval of the antenna and the RFID tags conflicts would affect the RFID system’s performance.

By monitoring the weight and moving time of individual hens using weighing sensors and RFID devices, poultry behaviours such as speed, ability to acquire food and resting time were detected, and by the K-means clustering method, chickens were classified as active, normal or sick (Zhang et al., Reference Zhang, Hu, Chen, Guo, Duan and Wang2016). An ultra-high-frequency RFID system was developed to monitor the feeding and nesting behaviours of 60 individual hens in enriched colony housing (Li et al., Reference Li, Zhao, Oliveira, Verhoijsen, Liu and Xin2017). Using the same experimental device, feeding behaviour was assessed with various feeder spaces (12.0, 9.5, 8.5 and 6.5 cm/hen) (Oliveira et al., Reference Oliveira, Xin and Wu2018). This type of research could contribute to housing system design, animal welfare and resource allocation.

Ranging behaviour in free-range housing

Currently, the free-range system is considered to be more natural and better for poultry welfare. However, each hen within a group has a different ranging behavioural pattern. To monitor the outdoor ranging behaviour of individual chickens, RFID systems have been used by attaching tags on chickens and putting antennas at pop holes, through which chickens move inside and outside (Gebhardt-Henrich et al., Reference Gebhardt-Henrich, Toscano and Frohlich2014; Campbell et al., Reference Campbell, Hinch, Dyall, Warin, Little and Lee2017; Larsen et al., Reference Larsen, Cronin, Gebhardt-Henrich, Smith, Hemsworth and Rault2017). The frequency and duration of visiting the outdoor areas are then calculated by analysing data from chickens walking over the antennas. These systems could be used in commercial farms and potentially track thousands of birds. In addition, an RFID system based on ultra-wideband technology was developed for monitoring 42 individual chickens’ locations in the 100 m × 100 m experimental field, which could calculate the position with 0.29 m of the median error and 68% of the registration rate (Stadig et al., Reference Stadig, Ampe, Rodenburg, Reubens, Maselyne, Zhuang, Criel and Tuyttens2018a). Monitoring of outdoor range use at the individual level allows for correlation between ranging and welfare indicators such as tonic immobility test, plumage damage, foot health and spatial cognition (Campbell et al., Reference Campbell, Hinch, Downing and Lee2016, Reference Campbell, Hinch, Downing and Lee2018a and Reference Campbell, Talk, Loh, Dyall and Lee2018b; Hartcher et al., Reference Hartcher, Hickey, Hemsworth, Cronin, Wilkinson and Singh2016; Larsen et al., Reference Larsen, Hemsworth, Cronin, Gebhardt-Henrich, Smith and Rault2018; Taylor et al., Reference Taylor, Hemsworth, Groves, Gebhardt-Henrich and Rault2018).

The impact of wearing a tag

Because the birds are small, the size, shape, weight and attachment method of tags might influence bird behaviour and the accuracy of the data. The behavioural and physiological responses of laying hens which wore backpacks containing automated monitoring devices were studied and showed that the backpacks only had a very minor effect on their behaviour after a short acclimation period (≤2 days) (Buijs et al., Reference Buijs, Booth, Richards, McGaughey, Nicol, Edgar and Tarlton2018). Furthermore, the effects on broiler behaviour, leg health, cleanliness and growth were evaluated with a tag worn on the back of birds for tracking location in a field (Stadig et al., Reference Stadig, Rodenburg, Ampe, Reubens and Tuyttens2018b). Six pens with 10 birds in each were observed with 5 birds fitted with a 36-g backpack and the other five with coloured markings to identify individuals. Footpad and hock dermatitis, gait and cleanliness were scored manually. These results indicated that birds wearing backpacks showed less activity early on, but no significant differences were observed in long-term behaviours between tagged birds and coloured birds. In summary, compared to the weight of chickens, a light monitoring tag has little impact on the behaviours of chickens after a period of adjustment. Wireless sensors are a reliable tool for detecting the location and activity of individual chickens.

Flock activity analysis

Through the number of changed pixels between two adjacent frames divided by the overall number of pixels in the image, the flock activity level can be calculated (Youssef et al., Reference Youssef, Exadaktylos and Berckmans2015; Fraess et al., Reference Fraess, Bench and Tierney2016). The method is heavily influenced by plumage differences. Group activity around the food trough was assessed by the percent pixel change (Fraess et al., Reference Fraess, Bench and Tierney2016). The study showed that the method could identify changes in activity level, identify abnormal activity away from the feeder and serve as an early health warning system.

Optical flow is a method to find the corresponding relationship between the previous frame and the current frame by analysing the change in pixels in the time domain of the image sequence and the correlation between adjacent frames, to calculate the motion of objects between adjacent frames. It is suitable for monitoring flock activity in the commercial farm. Using the optical flow patterns of chicken movements, infection by the human pathogen Campylobacter was detected in the early stages (Colles et al., Reference Colles, Cain, Nickson, Smith, Roberts, Maiden, Lunn and Dawkins2016). Optical flow data were collected, and the mean, variance, skew and kurtosis were calculated. Infected Campylobacter flocks had low mean optical flow and an apparent high kurtosis, and infection was detected 7 to 10 days earlier compared with faecal sample testing. This system has the potential for early health and welfare warnings that can benefit farmers and chickens. Using the camera-based optical flow method, early-stage footpad dermatitis and hock burn were predicted in 50 flocks from a commercial farm (Dawkins et al., Reference Dawkins, Roberts, Cain, Nickson and Donnelly2017). Optical flow analysed the movements of flocks, and lower skew and higher kurtosis values indicated heterogeneity in flock movement, which had predictive power for footpad dermatitis and hock burn.

In commercial farms, the activity and sudden variation of flock could be related to abnormal conditions, such as the equipment failure and diseases. Converting images into animal distribution index, the equipment failure in a broiler house could be found out in real time by the sudden changes of the distribution index (Kashiha et al., Reference Kashiha, Bahr, Vranken, Hong and Berckmans2014). With a uniform moderate walking pace through the flock, four variables were derived from the changes in activity before and after walking, so that the gait score could be predicted automatically (Silvera et al., Reference Silvera, Knowles, Butterworth, Berckmans, Vranken and Blokhuis2017). The occupation index was used instead of the distribution index, and the activity and occupation of the flock had correlation with the welfare scores for the footpad lesions and hock burns by human experts (Fernandez et al., Reference Fernandez, Norton, Tullo, van Hertem, Youssef, Exadaktylos, Vranken, Guarino and Berckmans2018). Moreover, the precise fault location could be performed by these indices in specific areas such as feeding, drinking and resting.

By combining a computer vision system with a novel convolutional neural network (CNN) method, the flock behaviour in a commercial farm was monitored automatically with a Kinect camera (Pu et al., Reference Pu, Lian and Fan2018). Three categories around feeders, not crowded, slightly crowded and fairly crowded, were labelled manually among 17 135 colour images and 17 135 depth images, and a supervised learning CNN was trained. The experimental results show that our proposed CNN-based method can recognise the flock behaviour of chickens with an accuracy of 99.17%.

Behaviours of individuals

Gait score of an individual hen can be done automatically. A digital video camera was mounted above the centre of a testing corridor to record images. The image processing method could classify the number of lying events (NOL) and latency to lie (LTL) of broilers (Aydin, Reference Aydin2015, Reference Aydin2017a and Reference Aydin2017b). The background subtraction method was applied to segment the shape, and then an elliptical shape model was used to define the size and orientation of the chicken (Aydin, Reference Aydin2015). Compared with the gait scores calculated manually from 250 broilers, the automatic monitoring system had 83% accuracy of identifying NOL. Subsequently, the image processing algorithm was improved by extracting feature variables (Aydin, Reference Aydin2017a). Automatically collected feature variables of broilers showed a clear correlation between feature variables and gait scores and were found to have a high level of accuracy starting from gait score three. Furthermore, instead of the 2D camera, a Kinect camera was used in the same experiment (Aydin, Reference Aydin2017b). Additional distance information between the broiler and the 3D camera was collected as broilers walked along a test corridor, and data were extracted to detect NOL and assess LTL with the back posture. The accuracy of NOL classification reached 93% compared to manual scoring.

In addition to RFID, image processing is another technology that has been considered to detect nest use. It can detect double nest occupation (Zaninelli et al., Reference Zaninelli, Costa, Tangorra, Rossi, Agazzi and Savoini2015 and Reference Zaninelli, Redaelli, Luzi, Mitchell, Bontempo, Cattaneo, Dell′Orto and Savoini2018), while RFID cannot. Photographs taken in a nest that was painted black, with the number of colour pixels counted and compared with the threshold double nest occupation, could detect whether there were two or more hens in a nest (Zaninelli et al., Reference Zaninelli, Costa, Tangorra, Rossi, Agazzi and Savoini2015). Later, with a thermographic camera to identify laying hens in a closed room (Zaninelli et al., Reference Zaninelli, Redaelli, Tirloni, Bernardi, Dell’Orto and Savoini2016 and Reference Zaninelli, Redaelli, Luzi, Bontempo, Dell’Orto and Savoini2017), this team developed a better ‘nest usage sensor’, which could count the number of hens in the nest (Zaninelli et al., Reference Zaninelli, Redaelli, Luzi, Mitchell, Bontempo, Cattaneo, Dell′Orto and Savoini2018). The thermographic images were processed by binarisation and coloured, and the number of colour pixels was counted to detect whether there were more than one hen in the nest. If there had a multiple nest occupation, pattern recognition was performed to make sure how many hens were in the nest.

The image processing algorithm could calculate the number of hens using an IR camera in an automatic lighting preference test system (Li et al., Reference Li, Li, Shi, Zhao and Ma2018). Original colour images were first cropped, converted into grey scale and then converted into binary images. The number of hens could be concluded by the number of white pixels. The accuracy of the image algorithm was 71.23%, due to the number of pixels changing significantly for one hen when feeding, wing flapping and preening.

The area required by each hen for the behaviours of standing, lying, perching, wing flapping and dust bathing was studied using ImageJ software and MATLAB (Riddle et al., Reference Riddle, Ali, Campbell and Siegford2018). However, it required manual selection of perfect images. Diseased broilers were predicted and classified automatically by the SVM model (Zhuang et al., Reference Zhuang, Bi, Guo, Wu and Zhang2018). To obtain the posture features, images were segmented by K-means clustering, and the skeleton structure was obtained by the thinning method. Superior to other machine learning (ML) algorithms, the SVM model obtained an accuracy rate of 99.47%.

Weight prediction

An automatic weight prediction system based on a 3D camera was developed and assessed on a commercial farm (Mortensen et al., Reference Mortensen, Lisouski and Ahrendt2016). The algorithm consisted of a range-based watershed algorithm and was predicted by a Bayesian artificial neural network. The system was able to weigh several chickens simultaneously. Due to the overlapping of broilers in the image, better prediction could be improved by a better segmentation method. Moreover, to estimate the weight of live broiler chickens, a machine vision method with an artificial neural network (ANN) was proposed (Amraei et al., Reference Amraei, Abdanan Mehdizadeh and Salari2017). Compared to Levenberg–Marquardt, scaled conjugate gradient and gradient descent, Bayesian regulation was the best network for the prediction of broiler weight. The maximum error of the system was less than 50 g. The aforementioned systems weighed individual broilers.