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Identifying rale sounds in chickens using audio signals for early disease detection in poultry
Abstract
Extreme learning machine (ELM) and support vector machine (SVM) classifiers are developed to detect rales (a gurgling sound that is a symptom of respiratory diseases in poultry). These classifiers operate on Mel-scaled spectral features calculated from recordings of healthy and sick chickens during a vaccine trial. Twenty minutes of labeled data were used to train and test the classifiers, then they were run on the full 25 days of continuous recordings from the healthy and sick chickens. The resulting detection rate follows the course of the disease and clearly distinguishes between the healthy and sick chickens. These results improve on our previous findings from the same data, and demonstrate the potential for automated acoustic monitoring of the health of commercial flocks.
... Swine [30] Sound Based Positive cough recognition Laboratory Remote audio labeling [31] Sound Based Positive cough recognition Laboratory Remote audio labeling [32] Sound Based Accuracy Field Live audio labeling [33] Sound Based Accuracy Field Live audio labeling [34] Sound Based Correct identification ratio Laboratory Remote audio labeling [35] Sound Based Correct identification ratio Field Remote audio labeling [36] Sound Based Accuracy Field Live audio labeling [37] Sound Based Sensitivity, Precision, Accuracy, and cough detection rate Field Remote audio labeling and blood analysis [15] Sound Based Sensitivity, Precision, cough detection rate, and F1-score Field Video labeling and blood analysis [38] Sound Based Word error rate Laboratory Remote audio labeling [39] Sound Based Sensitivity, Specificity, Precision, Accuracy, and F1-score Field Remote audio labeling [40] Sound Based Sensitivity, Specificity, Precision, Accuracy, and F1-score Field Remote audio labeling [41] Sound Based Sensitivity, Precision, Accuracy, and F1-score Field Remote audio labeling Poultry [42] Sound Based Sensitivity, Specificity, and Accuracy Laboratory PCR [43] Sound Based Sensitivity, Precision, and Accuracy Laboratory Remote audio labeling [14] Sound Based Sensitivity, Specificity, and Precision Laboratory Remote audio labeling [44] Sound Based Accuracy Laboratory PCR [45] Sound Based Sensitivity, Precision, Accuracy, and F1-score Field Remote audio labeling [46] Sound Based Sensitivity, Precision, Accuracy, and F1-score Laboratory Video labeling and PCR Bovine [47] Image Sensitivity, Specificity, PPV, NPV, and Cut off value Field Clinical assessment [13] Sound Based Sensitivity, Specificity, and Precision Field Clinical assessment and blood analysis [48] Sound Based Sensitivity, Specificity, and Precision Field Blood analysis [16] Accelerometer Sensitivity, Specificity, Accuracy, and MCC Field Clinical assessment ...
... A total of eight studies were considered to have a high risk of bias: five swine production studies [32,33,37,38,41], two poultry production studies [43,44], and one for bovine production study [47]. Table 3 shows the specific information that was presented or not presented in each study. ...
... Table 3 shows the specific information that was presented or not presented in each study. [31] low [32] high [33] high [34] low [35] low [36] low [37] high [15] low [38] high [39] low [40] low [41] high Poultry [42] low [43] high [14] low [44] high [45] low [46] low Bovine [47] high [13] low [48] low [16] low -Information provided in the article. -Information not provided in the article. ...
Respiratory diseases commonly affect livestock species, negatively impacting animal’s productivity and welfare. The use of precision livestock farming (PLF) applied in respiratory disease detection has been developed for several species. The aim of this systematic review was to evaluate if PLF technologies can reliably monitor clinical signs or detect cases of respiratory diseases. A technology was considered reliable if high performance was achieved (sensitivity > 90% and specificity or precision > 90%) under field conditions and using a reliable reference test. Risk of bias was assessed, and only technologies tested in studies with low risk of bias were considered reliable. From 23 studies included—swine (13), poultry (6), and bovine (4) —only three complied with our reliability criteria; however, two of these were considered to have a high risk of bias. Thus, only one swine technology fully fit our criteria. Future studies should include field tests and use previously validated reference tests to assess technology’s performance. In conclusion, relying completely on PLF for monitoring respiratory diseases is still a challenge, though several technologies are promising, having high performance in field tests.
... Some scholars evaluated poultry health by detecting coughs, sneezes and rales (Carroll et al., 2014;Rizwan et al., 2016;Carpentier et al., 2019;Liu et al., 2020) while others evaluated poultry health using poultry vocalization (Huang et al., 2019;Mahdavian et al., 2020;Cuan et al., 2020). Sadeghi et al. (2015) proposed an intelligent method for the detection and classification of chickens infected by Clostridium perfringens type A based on their vocalization. ...
... The mel frequency cepstral coefficient (MFCC) is an audio feature widely used in audio and speech analysis. In recent years, the MFCC has been used increasingly more in animal and poultry sound research and has achieved good results (Rizwan et al., 2016;Bishop et al., 2019;Paseddula et al., 2021). ...
... Diseases of the respiratory system will affect the sounds of poultry to varying degrees; therefore, the sound produced by poultry can be used to study the health of poultry. Some studies evaluated the health of poultry using coughing and sneezing (Carroll et al., 2014;Rizwan et al., 2016;Carpentier et al., 2019;Liu et al., 2020), but coughing and sneezing comprise a small proportion of all sounds, making it difficult to obtain large samples; and poultry will cough and sneeze due to a variety of conditions. The causes of coughing and sneezing are uncertain and difficult to determine. ...
Newcastle disease (ND) is a common disease in poultry that has a great impact on poultry health and production. ND has destructive effects on the respiratory system, such as altering the acoustic features of bird vocalizations. For this reason, this research proposed a new method, the deep poultry vocalization network (DPVN), for the early detection of ND based on poultry vocalization. The method combined multiwindow spectral subtraction and high-pass filtering to reduce the influence of noise. In order to detect poultry vocalizations automatically, a multiple subband poultry vocalization endpoint detection method was proposed in this paper. The performance of the detection method was evaluated using the intersection-over-union (IOU) between the detected vocalizations and ground truth vocalizations. The recall of the detection method was 95.11%, and the precision was 96.54%. The audio features of poultry vocalizations are extracted by sound technology and used as the input of a deep learning network to recognize the vocalizations of poultry with Newcastle disease. Five different models were compared in the experiments. The method used in this paper achieves the best performance and the highest accuracy, recall and F1-score of 98.50%, 96.60% and 97.33%, respectively. The accuracies within the first, second, third and fourth days after infection were 82.15%, 90.00%, 93.60% and 98.50%, respectively. The experimental results show that the method proposed in this paper can be used to detect Newcastle disease in the early stage. It will be significant for improving animal welfare and the automated monitoring of poultry production.
... Welfare: Sensors, cameras, and microphones for acoustic monitoring represent a relatively new approach toward monitoring welfare and production in animal agriculture and poultry. Specifically, understanding how chickens behave using sensors, cameras, and microphones can offer producers a new tool in identifying disease [28,29]. In general, these types of automated continuous approaches in poultry are primarily at the research [28], development, and prototype level [7]. ...
... However, the potential applications are highly practical. For example, automated imagery analytics based on supervised machine learning-based approaches could be used to detect morbidity, presymptomatic signs of mortality [23,29], and ectoparasite infestation [28]. ...
... In short, it categorizes highdimensional continuous data. While it is currently primarily used in fields like bioinformatics, gene expression, and image recognition [47], SVM has been used to characterize the poultry meat via near-infrared (NIR) spectroscopy [42] and auditory sounds from chickens including gurgling sounds (e.g., rales) associated with several infectious respiratory agents in poultry [29]. ...
Leveraging data collected by commercial poultry requires a deep understanding of the data that are collected. Machine learning (ML)-based techniques are capable of "learning by finding" nonobvious associations and patterns in the data in order to create more reliable, accurate, explanatory, and predictive statistical models. This article provides practical definitions and examples of ML-based statistical approaches for the analysis of poultry production and poultry food safety-based data. In addition to summarizing the literature, two real examples of the supervised machine learning ensemble technique, random forest (RF), are provided with respect to predicting egg weights from a commercial layer farm and identifying the potential causes of a Salmonella outbreak from a commercial broiler facility. Specifically, as an example, for the prediction of egg weights, a training model and a test model were created, and a modification of RF was used to explore the ability to predict egg weights. Results identified multiple variables including Age, Farm Location, Body Weight, Total Eggs, Hens Housed, and House Style which were predictive of the continuous variable Egg Weight. With respect to the accuracy of the variable Egg Weight, the average error between the predicted and actual egg weight was determined to be less than 3%. With respect to broiler food safety, a relational database was constructed and a supervised RF model was developed to identify the predictors of Salmonella in a grow-out farm and associated broiler processing plant. Predictors of Salmonella that included livability, density of birds in the grow-out farm, and breeder age were identified. The task of choosing the most appropriate ML-based model(s) that accounts for the large number of variables common to the poultry industry and addresses the intricate interdependence between several production parameters and inputs while predicting multiple sequential outputs is complex. The use of ML techniques in combination with new data streams including sensors (e.g., visual and audio), IoT, and Web-scraping could offer a more comprehensive, efficient, and timely approach toward evaluating productivity, food safety, and profitability in commercial poultry.
... Chicken sounds can be used for detection of disease in poultry as it showed in [4], [5]. These systems used mel frequency cepstral coefficients (MFCCs), and decision tree [4] or support vector machines (SVM) and extreme learning machines [5] for rale sounds detection. ...
... Chicken sounds can be used for detection of disease in poultry as it showed in [4], [5]. These systems used mel frequency cepstral coefficients (MFCCs), and decision tree [4] or support vector machines (SVM) and extreme learning machines [5] for rale sounds detection. ...
... Solutions described in [3]- [5] are our starting point in development of our own system for stress detection in broiler chickens using their sounds. The rest of the paper is organized as following. ...
... Chicken sounds can be used for detection of disease in poultry as it showed in [4], [5]. These systems used mel frequency cepstral coefficients (MFCCs), and decision tree [4] or support vector machines (SVM) and extreme learning machines [5] for rale sounds detection. ...
... Chicken sounds can be used for detection of disease in poultry as it showed in [4], [5]. These systems used mel frequency cepstral coefficients (MFCCs), and decision tree [4] or support vector machines (SVM) and extreme learning machines [5] for rale sounds detection. ...
... Solutions described in [3]- [5] are our starting point in development of our own system for stress detection in broiler chickens using their sounds. The rest of the paper is organized as following. ...
The paper presents a system for stress detection in broiler chickens using audio data. The system is consisted of 4 classifiers adapted for 4 age groups of chickens (one for each week). These classifiers are based on support vector machines and as input features they use the features for voice quality evaluation and speech emotion recognition. Features are extracted on 50 ms long frames every 25 ms. Accuracy on the frame level of these classifiers varies from 63 to 83 %, depending on age group.
... Ren et al. (2009), used the HMM (Hidden Markov Model) to take a look at the connection between poultry sound patterns and stress stimulants to establish vocalization as a stress signal (Huang et al. 2019). Rizwan et al. (2016), used the support vector machine algorithm and the extreme learning machine algorithm and noticed that increased frequencies of rales were detected by both algorithms, but less false positive results were shown by the support vector machine (Ren et al. 2009). In a study on young chicks, it was found that communications between them and the hens were established through different sounds, which were identified as sounds of stress, threats, submissiveness, food, etc. and it can be said that bioacoustics can be incorporated into machine learning systems for further research and exploration of this particular domain (Rizwan et al. 2016). ...
... Rizwan et al. (2016), used the support vector machine algorithm and the extreme learning machine algorithm and noticed that increased frequencies of rales were detected by both algorithms, but less false positive results were shown by the support vector machine (Ren et al. 2009). In a study on young chicks, it was found that communications between them and the hens were established through different sounds, which were identified as sounds of stress, threats, submissiveness, food, etc. and it can be said that bioacoustics can be incorporated into machine learning systems for further research and exploration of this particular domain (Rizwan et al. 2016). ...
The poultry population has increased exponentially from 13.9 billion in the early 21st century to 26.56 billion by 2022 worldwide, emphasizing the vital nutritional and economic part of this section. Simultaneously, the poultry sector faces a considerable amount of tests from diseases such as avian influenza, coccidiosis, mycoplasmosis, etc. that cost the industry multibillion-dollar losses each year. The groundbreaking and revolutionary possibilities of artificial intelligence and machine learning in poultry disease detection and diagnosis are discussed in this review. By capitalizing on data from physiological and behavioral traits like movement, vocalization, body temperature, and excreta, AI algorithms can detect indications of illness and pathological conditions, which means strengthening disease management and bringing down economic losses. High-precision image and video processing, non-invasive monitoring, the use of thermal imaging, and accurate tracking of poultry to spot health issues are some of the crucial developments that have also aided in analyzing stress and other abnormalities. Incorporating new-age technologies into feasible, applicable, and economical diagnostic tools that have the potential to transform poultry well-being, enhance the welfare of poultry, and upgrade production as well as handling processes is discussed here. The upcoming prospects include global partnerships, better data analytics, and extended research or studies for the management of diseases and behavioral anomalies in all poultry species. The collaboration of AI, machine learning, and biotechnology holds colossal promise for the poultry sector, guaranteeing food safety and ensuring public health.
... Several recent studies propose automated acoustic approaches for monitoring poultry welfare, health and productivity in real time, in order to promote earlier husbandry interventions [25]. For example, acoustic tools have been proposed to monitor growth [26], feed intake [27], infectious bronchitis [28,29], necrotic enteritis [30], thermal comfort [31,32] and disturbance [32]. Most use machine learning approaches for classification, with algorithms trained on group-level recordings of flocks differing in health or stress exposure. ...
... vigilance [34]; feeding [35]) or emotional states (frustration [36]; anticipation [37]). Moreover, specific sounds are linked to thermal discomfort [31] and pain [38], and certain reflexive sounds to respiratory diseases [29]. ...
Chicks (Gallus gallus domesticus) make a repetitive, high energy 'distress' call when stressed. Distress calls are a catch-all response to a range of environmental stressors, and elicit food calling and brooding from hens. Pharmacological and behavioural laboratory studies link expression of this call with negative affective state. As such, there is an a priori expectation that distress calls on farms indicate not only physical, but emotional welfare. Using whole-house recordings on 12 commercial broiler flocks (n = 25 090-26 510/flock), we show that early life (day 1-4 of placement) distress call rate can be simply and linearly estimated using a single acoustic parameter: spectral entropy. After filtering to remove low-frequency machinery noise, spectral entropy per minute of recording had a correlation of -0.88 with a manual distress call count. In videos collected on days 1-3, age-specific behavioural correlates of distress calling were identified: calling was prevalent (spectral entropy low) when foraging/drinking were high on day 1, but when chicks exhibited thermoregulatory behaviours or were behaviourally asynchronous thereafter. Crucially, spectral entropy was predictive of important commercial and welfare-relevant measures: low median daily spectral entropy predicted low weight gain and high mortality, not only into the next day, but towards the end of production. Further research is required to identify what triggers, and thus could alleviate, distress calling in broiler chicks. However, within the field of precision livestock farming, this work shows the potential for simple descriptors of the overall acoustic environment to be a novel, tractable and real-time 'iceberg indicator' of current and future welfare.
... The application of sound analysis techniques has been widely studied ( Montevecchi et al., 1973;Marx et al., 2001;Feltenstein et al., 2002) to measure and analyze the amplitude and frequency of animals sounds ( Moura et al., 2008). It is perceived that automated animal monitoring with images or sounds can potentially be used to support farmers in animal husbandry ( Halachmi et al., 2002;Ismayilova et al., 2013;Rizwan et al., 2016); indeed, audio and image processing were applied to several animal species ( Bardeli et al., 2010;Curtin et al., 2014;Bowling et al., 2017). ...
... However, with the increasing in the body weight, the frequency level of the vocalizations emitted during the last wk of the cycle production decrease (1,100 Hz), and the background noise in the poultry house covers and partially masks birds' vocalizations, affecting the sound analysis. This is consistent with findings of Bardeli et al. (2010), Rizwan et al. (2016) and Bowling et al. (2017) that relate the increasing in the body weight to the frequency level of the vocalization emitted. ...
The pattern of body weight gain during the commercial growing of broiler chickens is important to understand growth and feed conversion ratio of each flock.
The application of sound analysis techniques has been widely studied to measure and analyze the amplitude and frequency of animal sounds. Previous studies have shown a significant correlation (P ≤ 0.001) between the frequency of vocalization and the age and weight of broilers. Therefore, the aim of this study was to identify and validate a model that describes the growth rate of broiler chickens based on the peak frequency of their vocalizations and to explore the possibility to develop a tool capable of automatically detecting the growth of the chickens based on the frequency of their vocalizations during the production cycle. It is part of an overall goal to develop a Precision Livestock Farming tool that assists farmers in monitoring the growth of broiler chickens during the production cycle. In the present study, sounds and body weight were continuously recorded in an intensive broiler farm during 5 production cycles. For each cycle the peak frequencies of the chicken vocalizations were used to estimate the weight and then they were compared with the observed weight of the birds automatically measured using on farm automated weighing devices. No significant difference is shown between expected and observed weights along the entire production cycles; this trend was confirmed by the correlation coefficient between expected and observed weights (r = 96%, P value ≤ 0.001).
The identified model used to predict the weight as a function of the peak frequency confirmed that bird weight might be predicted by the frequency analysis of the sounds emitted at farm level. Even if the precision of the weighing method based on sounds investigated in this study has to be improved, it gives a reasonable indication regarding the growth of broilers opening a new scenario in monitoring systems in broiler houses.
... D. Cheng et al. 8) used recurrent neural network (RNN) and CNN model to analyze the voices of sick chickens for identification of respiratory issues in poultry and achieved 97.4% accuracy. M. Rizwan et al. 68) used support vector machine (SVM) and extreme learning machine (ELM) classifiers, achieving 97.6% accuracy. The research conducted by Cuan et al. 56) , early detection of Newcastle disease (ND) was successfully accomplished through the analysis of chicken sounds. ...
... The model reported by Lee et al. (2022) was able to predict internal temperature conditions with an error <1%. Several poultry applications of SVM models to predict behaviour and welfare from audio data also exist such as detecting and classifying the stress of laying hens from changes in vocalizations (Lee et al., 2015), assessing thermal comfort conditions for laying hens (Du et al., 2020), and detecting respiratory disease using sounds (Rizwan et al., 2016). These SVM models can also be used to evaluate disease from image data, for example to identify avian pox (Hemalatha et al., 2014) or hock burns (Hepworth et al., 2012) in broiler chickens. ...
Within poultry production systems, models have provided vital decision support, opportunity analysis, and performance optimization capabilities to nutritionists and producers for decades. In recent years, due to the advancement of digital and sensor technologies, 'Big Data' streams have emerged, optimally positioned to be analyzed by machine-learning (ML) modeling approaches, with strengths in forecasting and prediction. This review explores the evolution of empirical and mechanistic models in poultry production systems, and how these models may interact with new digital tools and technologies. This review will also examine the emergence of ML and Big Data in the poultry production sector, and the emergence of precision feeding and automation of poultry production systems. There are several promising directions for the field, including: (1) application of Big Data analytics (e.g., sensor-based technologies, precision feeding systems) and ML methodologies (e.g., unsupervised and supervised learning algorithms) to feed more precisely to production targets given a 'known' individual animal, and (2) combination and hybridization of data-driven and mechanistic modeling approaches to bridge decision support with improved forecasting capabilities.
... Detection of infections with pathogenic microorganisms is also possible with this technology. The frequency of rales produced by chickens infected with infectious bronchitis virus (IBV) has been shown experimentally to enable detection of infections before clinical signs are evident [24,25]. Sadeghi et al. have recorded broiler vocalizations in healthy and Clostridium-perfringens-infected birds. ...
Simple Summary
In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges related to pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks of animal infectious diseases and zoonoses. The increase in poultry production must be achieved mainly through optimization and increased efficiency. The increasing ability to generate large amounts of data (“big data”)—coupled with the availability of tools and computational power to store, share, integrate, and analyze data with automatic and flexible algorithms—offers an unprecedented opportunity to develop tools to maximize farm profitability, reduce socio-environmental impacts, and increase animal and human health and welfare. The present work reviews the application of sensor technologies, specifically, the principles and benefits of advanced statistical techniques and their use in developing effective and reliable classification and prediction models to benefit the farming system. Finally, recent progress in pathogen genome sequencing and analysis is discussed, highlighting practical applications in epidemiological tracking and control strategies.
Abstract
In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges. Pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks and effects of animal infectious diseases and zoonoses are several topics that have received attention from authorities and the public. The increase in poultry production must be achieved mainly through optimization and increased efficiency. The increasing ability to generate large amounts of data (“big data”) is pervasive in both modern society and the farming industry. Information accessibility—coupled with the availability of tools and computational power to store, share, integrate, and analyze data with automatic and flexible algorithms—offers an unprecedented opportunity to develop tools to maximize farm profitability, reduce socio-environmental impacts, and increase animal and human health and welfare. A detailed description of all topics and applications of big data analysis in poultry farming would be infeasible. Therefore, the present work briefly reviews the application of sensor technologies, such as optical, acoustic, and wearable sensors, as well as infrared thermal imaging and optical flow, to poultry farming. The principles and benefits of advanced statistical techniques, such as machine learning and deep learning, and their use in developing effective and reliable classification and prediction models to benefit the farming system, are also discussed. Finally, recent progress in pathogen genome sequencing and analysis is discussed, highlighting practical applications in epidemiological tracking, and reconstruction of microorganisms’ population dynamics, evolution, and spread. The benefits of the objective evaluation of the effectiveness of applied control strategies are also considered. Although human-artificial intelligence collaborations in the livestock sector can be frightening because they require farmers and employees in the sector to adapt to new roles, challenges, and competencies—and because several unknowns, limitations, and open-ended questions are inevitable—their overall benefits appear to be far greater than their drawbacks. As more farms and companies connect to technology, artificial intelligence (AI) and sensing technologies will begin to play a greater role in identifying patterns and solutions to pressing problems in modern animal farming, thus providing remarkable production-based and commercial advantages. Moreover, the combination of diverse sources and types of data will also become fundamental for the development of predictive models able to anticipate, rather than merely detect, disease occurrence. The increasing availability of sensors, infrastructures, and tools for big data collection, storage, sharing, and analysis—together with the use of open standards and integration with pathogen molecular epidemiology—have the potential to address the major challenge of producing higher-quality, more healthful food on a larger scale in a more sustainable manner, thereby protecting ecosystems, preserving natural resources, and improving animal and human welfare and health.
... Avian influenza Imaging (thermal images) experimental setting [29] Campylobacter jejuni imaging (flock movement-optical flow) dataset from broiler buildings [30] Clostridium perfringens sound analysis (vocalizations) experimental setting [31] Coccidiosis sensor (volatile organic compounds) experimental setting + broiler building [32] Coccidiosis sensor (volatile organic compounds) dataset from broiler buildings [33] Coccidiosis + Salmonella spp. imaging (feces) dataset of images [20] Ektoparasites wearable sensor (activity) dataset from poultry building [34] Infectious bronchitis sound analysis (rales) experimental setting [35] Infectious bronchitis sound analysis (rales) experimental setting [36] Infectious bronchitis + Newcastle disease sound analysis (vocalizations) experimental setting [37] Newcastle disease sound analysis (sneezes) experimental setting [38] Newcastle disease imaging (posture and mobility) experimental setting [39] Newcastle disease sound analysis (vocalizations) experimental setting [40] Non-specific, clinical signs imaging (feces) dataset of images [41] Non-specific, clinical signs imaging and sound analysis dataset of audio samples [42] Non-specific, clinical signs imaging (feces) dataset from broiler building [43] Non-specific, clinical signs imaging (head motion, appearance) experimental setting [44] Non-specific, clinical signs imaging (posture, appearance) experimental setting [45] Non-specific, clinical signs sound analysis (abnormal respiratory sounds) dataset from broiler building [46] Non-specific, clinical signs imaging (posture, appearance) dataset of images [47] Pasteurella spp. imaging (thermal images) experimental setting [48] * All studies were perfomed with chickens, except Noh et al. [29] who also used ducks. ...
Controlling infectious diseases is vital for poultry health and diagnostic methods are an indispensable feature to resolve disease etiologies and the impact of infectious agents on the host. Although the basic principles of disease diagnostics have not changed, the spectrum of poultry diseases constantly expanded, with the identification of new pathogens and improved knowledge on epidemiology and disease pathogenesis. In parallel, new technologies have been devised to identify and characterize infectious agents, but classical methods remain crucial, especially the isolation of pathogens and their further characterization in functional assays and studies. This review aims to highlight certain aspects of diagnosing infectious poultry pathogens, from the farm via the diagnostic laboratory and back, in order to close the circle. By this, the current knowledge will be summarized and future developments will be discussed in the context of applied state-of-the-art techniques. Overall, a common challenge is the increasing demand for infrastructure, skills and expertise. Divided into separate chapters, reflecting different disciplines, daily work implies the need to closely link technologies and human expertise in order to improve bird health, the production economy and to implement future intervention strategies for disease prevention.
... A sound signal contains many different audio features. Spectrogram and Mel frequency cepstral coefficients (MFCCs) are audio features widely used in audio analysis and speech processing and have achieved good results in various audio processing problems (Rizwan et al., 2016;Huang et al., 2019;Paseddula and Gangashetty, 2021). The spectrogram shows the characteristics of sound in the time domain and frequency domain. ...
Gender determination in chicks is an important task in poultry production and is helpful for precision feeding of different sexes. At present, most chicken sex identification methods need to be completed manually by professionals, which is time-consuming and laborious. In this paper, a method was designed to determine the sex of one-day-old chicks according to vocalizations. This method uses sound technology to detect chick vocalization and automatically detects vocalization endpoints by the double threshold method using three parameters: short-term energy, short-term zero crossing rate and duration. The audio features were extracted as the input of the three deep learning models for learning and classification. In the experiment, the training set was used to train the model, and the test set was used to calculate the detection results. The vocalizations of the training set and test set came from different chicks. In the gender detection of each vocalization, the accuracy of convolutional neural networks (CNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was 74.55%, 75.73% and 76.15%, respectively. The highest recall was 77.03% of GRU, and the highest specificity was 78.38% of LSTM. After that, the gender of chicks was predicted according to the vocalization detection results. The average accuracy values of CNN, LSTM and GRU for vocalization were 91.25%, 87.08% and 88.33%, respectively. The experimental results show that the method proposed in this paper can be used to detect the gender of chicks by vocalization, which is of great significance for automatic chick gender detection and intelligent poultry production.
... Despite research highlighting the potential for automated monitoring of vocalizations as a means to assess and monitor animal welfare states [6], progress has been slow. In chickens, most methods have focused on detecting issues associated with respiratory diseases or measuring growth [7][8][9][10]. However, due to the links between emotional states and types of vocalizations and recent advances in machine learning applied to audio data [3,[11][12][13][14][15], we hypothesized that automated detection of chicken distress calls would be feasible. ...
The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an ‘iceberg indicator’ of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.
... Decision tree is a kind of sequential model, which has been widely used to build classification models, as such models closely resemble human reasoning and are easy to understand [40]. For the same sound data, Whitaker et al. [41] applied dictionary learning and sparse coding methods to classify sound signals, and the accuracy was improved to 97.85%, and Rizwan et al. [42] developed an extreme learning machine (ELM) and support vector machine (SVM) classifiers to detect rales, and their accuracy was 97.1% and 97.6%, respectively. Banakar et al. [43] have shown that sound signals from poultry infected with different diseases would exhibit different acoustic characteristics that could be distinguished. ...
Global animal protein consumption has been steadily increasing as a result of population growth and the increasing demand for nutritious diets. The poultry industry provides a large portion of meat and eggs for human consumption. The early detection and warning of poultry infectious diseases play a critical role in the poultry breeding and production systems, improving animal welfare and reducing losses. However, inadequate methods for the early detection and prevention of infectious diseases in poultry farms sometimes fail to prevent decreased productivity and even widespread mortality. The health status of poultry is often reflected by its individual physiological, physical and behavioral clinical symptoms, such as higher body temperature resulting from fever, abnormal vocalization caused by respiratory disease and abnormal behaviors due to pathogenic infection. Therefore, the use of technologies for symptom detection can monitor the health status of broilers and laying hens in a continuous, noninvasive and automated way, and potentially assist in the early warning decision-making process. This review summarized recent literature on poultry disease detection and highlighted clinical symptom-monitoring technologies for sick poultry. The review concluded that current technologies are already showing their superiority to manual inspection, but the clinical symptom-based monitoring systems have not been fully utilized for on-farm early detection.
... More specifically, acoustic studies are interesting for detecting stress or panic states or abnormal noise on the farm. For example, teams of researchers have focused on identifying rales, characteristic symptoms of respiratory infections in poultry [142,143]. A recent study has developed, under experimental conditions, an algorithm for detecting sneezing in groups of 15 to 36 broilers, with an accuracy of 88% and sensitivity of 67% [144]. ...
Since the ban in January 2012 of conventional cages for egg production in the European Union (Council Directive 1999/74/EC), alternative systems such as floor, aviary, free-range, and organic systems have become increasingly common, reaching 50% of housing for hens in 2019. Despite the many advantages associated with non-cage systems, the shift to a housing system where laying hens are kept in larger groups and more complex environments has given rise to new challenges related to management, health, and welfare. This review examines the close relationships between damaging behaviours and health in modern husbandry systems for laying hens. These new housing conditions increase social interactions between animals. In cases of suboptimal rearing and/or housing and management conditions, damaging behaviour or infectious diseases are likely to spread to the whole flock. Additionally, health issues, and therefore stimulation of the immune system, may lead to the development of damaging behaviours, which in turn may result in impaired body conditions, leading to health and welfare issues. This raises the need to monitor both behaviour and health of laying hens in order to intervene as quickly as possible to preserve both the welfare and health of the animals.
... Despite research highlighting the potential for automated monitoring of vocalisations as a means to assess and monitor animal welfare states [6], progress has been slow. In chickens, most methods have focused on detecting issues associated with respiratory diseases or measuring growth [7][8][9][10]. However, due to the links between emotional states and types of vocalisations and recent advances in machine learning applied to audio data [3,[11][12][13][14][15], we hypothesised that automated detection of chicken distress calls would be feasible. ...
The annual global production of chickens exceeds 25 billion birds, and they are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an "iceberg indicator" of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotations, which is very labour-intensive and time-consuming. Thus, a novel light-VGG11 was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on intensive chicken farms. The light-VGG11 was modified from VGG11 with a significantly smaller size in parameters (9.3 million vs 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e., precision (94.58%), recall (94.89%), F1-score (94.73%), and accuracy (95.07%), therefore more useful for model deployment in practice. To further improve the light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e., time masking, frequency masking, mixed spectrograms of the same class, and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. In terms of precision livestock farming, our research opens new opportunities for developing technologies used to monitor the output of distress calls in large, commercial chicken flocks.
... The drive towards reduced FCR motivates farmers to monitor the performance better and understand the development of their animals. Over the past decades, a variety of classification and detection methods have been developed in poultry farming including acoustic resonance [12][13][14][15][16], robotics [17], remote sensing [18], Wireless Sensor Networks (WSNs) [19][20][21][22][23][24][25][26], and computer vision . It should be noted that this review highlights on the computer vision component in poultry farming. ...
The productivity and profitability of poultry farming are crucial to support its affordability issues in food security. Criteria in productivity measurement, including Feed Conversion Ratio (FCR) calculation, whereas economic management is essential for profitability. Hence, best management practices need to be implemented throughout the growth period for optimizing the poultry performance. This review provides a comprehensive overview of computer vision technology for poultry industry research. This review relies on the use of several online databases to identify key works in the area of computer vision in a poultry farm. We recommend our search by focusing on four keywords, ‘computer vision’ and ‘poultry’ or ‘chicken’ or ‘broiler’ that had been published between 2010 and early 2020 with open access provided by University Teknologi Malaysia only. All the selected papers were manually examined and sorted to determine their relevance to computer vision in a poultry farm. We focus on the latest developments by focusing on the hardware and software parts used to analyze the poultry data with some examples of various representative studies on poultry farming. Notably, hardware parts can be classified into camera types, lighting units and camera position, whereas software parts can be categorized into data acquisition and analysis software types as well as data processing and analysis methods that can be implemented into the software types. This paper concludes by highlighting the future works and key challenges that needed to be addressed to assure the quality of this technology prior to the successful implementation of the poultry industry.
... Symptoms of disease can be detected with sound analysis, for example coughing in pigs [56][57][58] and in calves [59] and rale sounds in chickens, as symptoms for lung disease [60]. Lameness in cows, pigs or poultry can be detected with force plates or pressure mats [38,61,62]. ...
Animal welfare is a multidimensional phenomenon and currently its on-farm assessment requires complex, multidimensional frameworks involving farm audits which are time-consuming, infrequent and expensive. The core principle of precision agriculture is to use sensor technologies to improve the efficiency of resource use by targeting resources to where they give a benefit. Precision livestock farming (PLF) enables farm animal management to move away from the group level to monitoring and managing individual animals. A range of precision livestock monitoring and control technologies have been developed, primarily to improve livestock production efficiency. Examples include using camera systems monitoring the movement of housed broiler chickens to detect problems with feeding systems or disease and leg-mounted accelerometers enabling the detection of the early stages of lameness in dairy cows. These systems are already improving farm animal welfare by, for example, improving the detection of health issues enabling more rapid treatment, or the detection of problems with feeding systems helping to reduce the risk of hunger. Environmental monitoring and control in buildings can improve animal comfort, and automatic milking systems facilitate animal choice and improve human-animal interactions. Although these precision livestock technologies monitor some parameters relevant to farm animal welfare (e.g. feeding, health), none of the systems yet provide the broad, multidimensional integration that is required to give a complete assessment of an animal's welfare. However, data from PLF sensors could potentially be integrated into automated animal welfare assessment systems, although further research is needed to define and validate this approach.
... Detecting infection with pathogenic microorganisms is also possible with this technology. The frequency of rales produced by chickens infected with infectious bronchitis virus has been shown experimentally to be able to detect infection before clinical signs are evident in infected chickens (Carroll et al., 2014;Rizwan et al., 2017). This method would be advantageous compared to conventional methods employed for disease detection such as visual inspection, as treatment or remedial actions can be initiated much sooner to inhibit further bird infection. ...
As the world’s population increases, demand for poultry products will continue to increase. To meet this demand, one candidate mechanism to increase production is to increase housing and manage more birds. However, this practice, along with labour shortages and increasing biosecurity practices will make it increasingly difficult for producers to monitor the production, health, and welfare status of all their birds. Employing smart poultry management systems is necessary to increase production while minimizing costs and the use of resources. Smart poultry management systems include precision livestock farming (PLF) technologies such as smart sensors, automation of farm processes, and data driven decision making platforms. Many new technologies will have great implications for poultry production in the areas of the poultry house environment, bird welfare, precision feeding, and rapid detection of infectious disease. As smart sensors collect data in real-time on a variety of parameters from poultry operations, large amounts of data will be generated. To make best use of this data, big data analytical tools must be employed to produce data driven decisions. Additionally, the devices that will be incorporated into smart poultry management systems will be connected to the Internet allowing for the formation of Internet of things (IoT) farm networks. IoT technologies allow for communication between farm sensors, devices, and equipment, and will lead to the automation of multiple farm procedures. The following review discusses the areas of impact that new smart sensor technologies will have on poultry operations and describes how sensor technology is related to big data analytics and IoT systems, and how these technologies can enhance production in the poultry industry. Additionally, challenges to the described systems and technologies will also be highlighted and discussed.
... Analysis of chicken vocalization might be used to provide an early warning of unhealthy conditions for chickens. The features and algorithms could be adjusted to apply to different commercial settings Whitaker et al., 2014;Rizwan et al., 2016). Similarly, Lee et al. (2015) presented an online-monitoring prototype which could detect the stress and classify it into types like physical and mental stress using vocalization of laying hens. ...
The advent of agricultural robotics research worldwide has brought substantial improvement for various applications. This article provides a comprehensive review of published research and development work, emphasizing robotics enabling machine capabilities. These machine capabilities of perception, reasoning and learning, communication, task planning and execution, and systems integration have opened possibilities for intelligent automation of current and future agricultural operations, including precision livestock farming. We have focused on the Agricultural Intelligent Automation Systems which have a high potential to be applied to agricultural production and processing, especially with applicability to poultry production. Most of the published work on agricultural robotics has been in the areas of perception and reasoning. The emphases have been in the identification of objects, evaluation of product quality, monitoring of plant and animal growth and development, yield prediction, and machine guidance. There has been limited published work on the task execution and systems integration aspects of agricultural robotics. Moreover, we have reviewed agricultural robotics research from 24 universities worldwide. Agricultural robots can be divided into three categories (monitor, harvester, and both) according to various functions. Several tables are presented to summarize the information on the key subject areas reviewed in this article. We have found that there are still many challenges that need to be addressed in robotizing agricultural tasks in general and in poultry production specifically. The most common challenges in robotics applications have been developing robots for specific agricultural tasks. Examples in poultry production include monitoring environmental conditions and chicken health, egg picking, and encouraging chicken movement. The approaches to addressing the technical needs have been creating intelligent movable machines for use alongside the chickens in poultry house. The most noticeable results include Octopus Poultry Safe (OPS) robot for sanitizing poultry houses autonomously, PoultryBot for picking floor eggs, and Spoutnic for training hens to move. This trend of research and development is expected to continue. An emerging research emphasis is systems approach to study the interactions of automated tasks to achieve high efficiency in whole poultry house management.
... Sensors, for example, are now common in the dairy sector (reviewed by Neethirajan [134]). Audio surveillance systems are also gradually making their way onto farms as a way of detecting disease, particularly respiratory diseases, at an early stage (e.g., pigs: [135]; [136]; poultry: [137]; cattle: [138]). In fact, an automated sound detection system (SoundTalks) can detect the onset of disease better than humans can, and therefore use of technology enables more timely and efficient treatment [139]. ...
Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.
... The second largest proportion of publications (20.95%) used vocalisations [59] or bird sounds. Bird sounds were pecking sounds (e.g., [60]), or in one publication, rale sounds [61]. ...
Precision livestock farming (PLF) systems have the potential to improve animal welfare through providing a continuous picture of welfare states in real time and enabling fast interventions that benefit the current flock. However, it remains unclear whether the goal of PLF development has been to improve welfare or increase production efficiency. The aims of this systematic literature review are to provide an overview of the current state of PLF in poultry farming and investigate whether the focus of PLF research has been to improve bird welfare. The study characteristics extracted from 264 peer-reviewed publications and conference proceedings suggest that poultry PLF has received increasing attention on a global scale, but is yet to become a widespread commercial reality. PLF development has most commonly focussed on broiler farming, followed by laying hens, and mainly involves the use of sensors (environmental and wearable) and cameras. More publications had animal health and welfare than production as either one of or the only goal, suggesting that PLF development so far has focussed on improving animal health and welfare. Future work should prioritise improving the rate of commercialisation of PLF systems, so that their potential to improve bird welfare might be realised.
... Marx et al. (2001) analysed the vocal behaviour of chicks when they were socially isolated. More specific to the monitoring of diseases, rales (gurgling noises) were observed for the detection of infectious bronchitis (Carroll et al., 2014;Rizwan et al., 2017). Banakar et al. (2016) used a support vector machine to classify sounds from Newcastle disease, infectious bronchitis and avian influenza. ...
... The algorithm used in the aforementioned study was trained to recognize rales, which are commonly produced from IBV infected chickens, and was able to detect increased rale frequency days before clinical signs of disease were evident. Also using IBV infected chicken recordings, Rizwan et al. (66) compared an extreme learning machine algorithm and a support vector machine algorithm, and determined that both could detect increased frequencies of rales, but the support vector machine algorithm demonstrated decreased incidences of false positive results for rale detection. Vocalization analysis of poultry is promising for early detection of infectious disease and could be potentially used for high and low pathogenic AIV, as rales can be a sign of infection in chickens (53). ...
Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched.
Agriculture and Food Science Book series aims to bring together leading academic scientists, researchers and research scholars to publish their experiences and research results on all aspects of Agriculture and Food Science. It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Agriculture and Food Science. High quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of Agriculture and Food Science are cordially invited for publication.
The rapid growth of human population and frequent changes in environmental conditions pose challenges in providing nutrient-rich food to the current population. The demand for poultry products is increasing exponentially as they are a good source of low-cost proteins. Ensuring the well-being of birds and delivering nutritious poultry products is essential for both current and future food security needs and sustainable agriculture. This article is motivated by the potential of emerging digital technologies in agriculture to provide innovative solutions to challenges faced by farmers, particularly in poultry farming. It aims to shed light on various issues encountered by farmers like diseases on poultry farms and offers valuable insights for researchers seeking to address these challenges using the advancements in digital technologies. Precision farming in poultry involves leveraging technologies such as the internet of things, artificial intelligence, and edge computing to enhance animal health management. A smart poultry farm maintains the farm environment and detects diseases at early stage in the chickens. The farm resources are utilized optimally in the smart poultry farm. This research work provides a systematic literature review of intelligent systems designed for (i) poultry birds’ health and welfare management using technologies like the internet of things (IoT), artificial intelligence, cloud computing, and edge computing and (ii) early disease detection, estimation of weight, and feeding behavior of birds with the use of computer vision and vocalization analysis. This article also explores various sensors employed in the development of IoT infrastructure. It has been demonstrated in the literature that modern digital technologies automate the management operations of poultry farms. As a result, the farmer’s income increases, and high-quality food products are available to people at low cost.
Livestock and poultry production and supply is one of the significant food sectors in which more production can lead to a decrease in dependence on exports and earning foreign exchange. Poultry farming is a vital industry for sustainable food supply in all countries. In this research, intelligent applications and solutions in the poultry industry are identified and prioritized using the simultaneous evaluation of criteria and alternatives (SECA) method based on criteria representing the sustainable development. Analysis showed that eighteen principal fields of intelligent solutions are identified in the poultry industry. The weights obtained for sustainable development criteria based on the SECA method are economic (0.351), social (0.3383), and environmental (0.3065) in order of value. Economic sustainability should be most important in implementing smart solutions-based projects in the poultry industry. One of the main challenges of the agricultural sector, especially the poultry industry, is traditional production utilization which leads to the overuse of land capacity. Globalization trends, climate changes, moving from a fossil fuel-based economy to an environment-based economy, competition for land, freshwater, and labor shortage have also led to more complications in supplying nutrition. Considering the potential of smart solutions in realizing sustainable development objectives, it is suggested to focus more on the environmental aspects of poultry industry projects.
Non-invasive measures have a critical role in precision livestock and poultry farming as they can reduce animal stress and provide continuous monitoring. Animal activity can reflect physical and mental states as well as health conditions. If any problems are detected, an early warning will be provided for necessary actions. The objective of this study was to identify avian diseases by using thermal-image processing and machine learning. Four groups of 14-day-old Ross 308 Broilers (20 birds per group) were used. Two groups were infected with one of the following diseases: Newcastle Disease (ND) and Avian Influenza (AI), and the other two were considered control groups. Thermal images were captured every 8 h and processed with MATLAB. After de-noising and removing the background, 23 statistical features were extracted, and the best features were selected using the improved distance evaluation method. Support vector machine (SVM) and artificial neural networks (ANN) were developed as classifiers. Results indicated that the former classifier outperformed the latter for disease classification. The Dempster-Shafer evidence theory was used as the data fusion stage if neither ANN nor SVM detected the diseases with acceptable accuracy. The final SVM-based framework achieved 97.2% and 100% accuracy for classifying AI and ND, respectively, within 24 h after virus infection. The proposed method is an innovative procedure for the timely identification of avian diseases to support early intervention.
Each government is looking for to provide the best services to establish efficiency and quality of performance. This goal could be accomplished by improving the service performance of entire sectors in society. The government of Syria has realized the importance of moving in the direction of information technology. Therefore, E-governance initiatives were launched in Syria as a part of overall country information technology in 20 s century. Each government sector has since upgraded the performance by having its websites and e-services application. However, there are gaps and loose connections exist among the sectors, which has accordingly tarnished the image of Syrian E-governance. This has led to significant questions about the requirement of modification and enhancement of such service. Hence, the purpose of this research is to investigate and explore the factors that drive the E-governance implementation and affect government performance as well as the government-citizen relationship in Syria.KeywordsE-governanceGovernment of SyriaGovernment-Citizen
Food stability has always received worldwide attention, especially in the development of the poultry industry. However, the poultry’s diseases have caused the loss of the poultry population and direct income of the owners. In this work, a system comprising of a website and a mobile application has been developed to support disease identification in chickens; the disease spread locations can be managed and traced with maps. In addition, the system allows farmers to make contact and get support from experts through chat, voice calls, and video calls. It also has some e-commerce functions for improving sales of poultry’s owners. The system uses the improved ResNet-50 model, with an accuracy of about 93.56%.KeywordsDisease identification in chickensIdentify disease spotsApplication for livestockResNet-50
The advent of digital technologies has brought substantial improvements in various domains. This article provides a comprehensive review of research emphasizing AI-enabled IoT applications in poultry health and welfare management. This study focused on poultry welfare since modern poultry management is confronted with issues relating to standardized parameters for welfare assessment and robust monitoring systems, particularly for broilers' health and disease outbreak prevention. Evidence has shown that modern digital technologies have high possibilities for intelligent automation of current and future poultry management operations to facilitate high-quality and low-cost poultry production. Therefore, this study presents a systematic review of the current state-of-the-art AI-enabled IoT systems and their recent advances in developing intelligent systems in this domain. Also, the study provides an overview of the critical applications of identified digital technologies in poultry welfare management. Lastly, the study discusses the challenges and opportunities of AI and IoT in poultry farming.
Poultry is the world’s most widespread bird species and the major contributor to ammonia and greenhouse gases emission. In order to improve poultry, productivity enterprises used new technological innovations to manage and monitor poultry farms. The objective of the study is to analyse the existing smart poultry management systems that allow taking a decision for the most appropriate feeding process and the lowest level of CO2 and NH3 emission. A smart poultry management system is a crucial component of modern poultry farm. The research directions and technologies related to smart poultry management systems are analysed, which are used for poultry productivity, health and welfare prediction. There is not much research on the use of a multi-criteria decision model in poultry farming, where productivity and welfare improvement and greenhouse gases emission reduction are taking into account at the same time. The necessary challenges of the smart platform implementation are designed for the improvement of poultry productivity and reduction of GHG emissions.
Cyber World has become accessible, public and commonly used to distribute and exchange messages between malicious actors, terrorists, and illegally motivated persons. Electronic mail is one of the most frequently used transfers of information on internet media. E-mails are the most important digital proof that courts in various countries and communities use to condemn and that enables researchers to work continually to improve e-mail analysis using state-of-the-art technology to find digital evidence from e-mails. This work introduces a distinctive technology to analyze emails. It is based on consecutive phases, starting with data processing, extraction, compilation, then implementing the SWARM algorithm to adjust the output and to transfer these electronic mails for realistic and precise results by adjusting the support algorithm of vector machines. For email forensic analysis this system includes all the sentiment terms plus positives and negatives cases. It can deal with the machine learning algorithm (Sent WordNet 3.0). Enron Data set is used to test the proposed framework. In the best case, a high accuracy rate is 92%.
The development of artificial poultry eggs has facilitated farming that allows it to become not only higher productive industry but also lower production costs in agriculture. In recent years, a lot of advanced technology has been applied in high-tech agriculture. The type of automatic incubator can adjust the temperature and humidity factors to achieve high efficiency. In the paper, we present details design of the parameter monitoring system in an industrial incubator with support vector machine (SVM) model in detecting sick chickens based on voice processing. These parameters sending to the central server for monitoring and training models to classify sounds will be used to predict healthy. Experimental results show that the proposed model meets real-time requirements with accuracy over 90% and processing time of less than one minute.
Nowadays, food security is essential in human life, especially for poultry meat. Therefore, the poultry raising is growing over years. This leads to the development of diseases on poultry, resulting in potentially great harm to human and the surrounding environment. It is estimated that when the diseases spread, the economic and environmental damages are relatively large. In addition, small-scale animal husbandry and an automated process to identify diseased chickens are essential. Therefore, this work presents an application of machine learning algorithms for automatic poultry disease identification. Here, the deep convolutional neural networks (CNNs) namely VGGNet and ResNet are used. The algorithms can identify four common diseases in chickens namely Avian Pox, Infectious Laryngotracheitis, Newscalte, and Marek against healthy ones. The obtained experimental results indicate that the highest achievable accuracies are 74.1% and 66.91% for VGGNet-16 and ResNet-50 respectively… The initial results showed positive results, serving the needs of the building and improving the model to achieve higher results.
The Internet has become open, public and widely used as a source of data transmission and exchanging messages between criminals, terrorists and those who have illegal motivations. Moreover, it can be used for exchanging important data between various military and financial institutions, or even ordinary citizens. One of the important means of exchanging information widely used on the Internet medium is the e-mail. Email messages are digital evidence that has been become one of the important means to adopt by courts in many countries and societies as evidence relied upon in condemnation, that prompts the researchers to work continuously to develop email analysis tool using the latest technologies to find digital evidence from email messages to assist the forensic expertise into to analyze email groups .This work presents a distinct technique for analyzing and classifying emails based on data processing and extraction, trimming, and refinement, clustering, then using the SWARM algorithm to improve the performance and then adapting support vector machine algorithm to classify these emails to obtain practical and accurate results. This framework, also proposes a hybrid English lexical Dictionary (SentiWordNet 3.0) for email forensic analysis, it contains all the sentiwords such as positive and negative and can deal with the Machine Learning algorithm. The proposed system is capable of learning in an environment with large and variable data. To test the proposed system will be select available data which is Enron Data set. A high accuracy rate is 92% was obtained in best case. The experiment is conducted the Enron email dataset corpus (May 7, 2015 Version of the dataset).
Abstract
Vocalisations carry emotional, physiological, and individual information. This suggests that they may serve as potentially useful indicators for inferring animal welfare. At the same time, automated methods for analysing and classifying sound have developed rapidly, particularly in the fields of ecology, conservation, and sound scene classification. These methods are already used to automatically classify animal vocalisations, for example in identifying animal species and estimating numbers of individuals. Despite this potential, they have not yet found widespread application in animal welfare monitoring. In this review, we first discuss current trends in sound analysis for ecology, conservation, and sound classification. Following this we detail the vocalisations produced by three of the most important farm livestock species: chickens (Gallus gallus domesticus), pigs (Sus scrofa domesticus), and cattle (Bos taurus). Finally, we describe how these methods can be applied to monitor animal welfare with new potential for developing automated methods for large-scale farming.
This paper presents a method for automatic detection of fish sounds in an underwater environment. There exist two difficulties: (i) features and classifiers that provide good detection results differ depending on the underwater environment and (ii) there are cases where a large amount of training data that is necessary for supervised machine learning cannot be prepared. A method presented in this paper (the proposed hybrid method) overcomes these difficulties as follows. First, novel logistic regression (NLR) is derived via adaptive feature weighting by focusing on the accuracy of classification results by multiple classifiers, support vector machine (SVM), and k-nearest neighbors (k-NN). Although there are cases where SVM or k-NN cannot work well due to divergence of useful features, NLR can produce complementary results. Second, the proposed hybrid method performs multi-stage classification with consideration of the accuracy of SVM, k-NN, and NLR. The multi-stage acquisition of reliable results works adaptively according to the underwater environment to reduce performance degradation due to diversity of useful classifiers even if abundant training data cannot be prepared. Experiments on underwater recordings including sounds of Sciaenidae such as silver croakers (Pennahia argentata) and blue drums (Nibea mitsukurii) show the effectiveness of the proposed hybrid method.
Pig vocalisations convey information about their current state of health and welfare. Continuously monitoring these vocalisations can provide useful information for the farmer. For instance, pig screams can indicate stressful situations. When monitoring screams, other sounds can interfere with scream detection. Therefore, identifying screams from other sounds is essential. The objective of this study was to understand which sound features define a scream. Therefore, a method to detect screams based on sound features with physical meaning and explicit rules was developed. To achieve this, 7 hours of labelled data from 24 pigs was used. The developed detection method attained 72% sensitivity, 91% specificity and 83% precision. As a result, the detection method showed that screams contain the following features discerning them from other sounds: a formant structure, adequate power, high frequency content, sufficient variability and duration.
Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.
Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.
Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution.
Bird song can be divided into a sequence of syllabic elements. We investigate the possibility of bird species recognition based on the syllable pair histogram of the song. This representation compresses the variable-length syllable sequence into a fixed-dimensional feature vector. The histogram is computed by means of Gaussian syllable prototypes which are automatically found given the song data and the dissimilarity measure of syllables. Our representation captures the use of the syllable alphabet and also some temporal structure of the song. We demonstrate the method in bird species recognition with song patterns obtained from fifty individuals belonging to four common passerine bird species.
Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks.
Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysis techniques in recent years. Some common tasks in bioacoustics are repertoire determination, call detection, individual identification, stress detection, and behavior correlation. Each research study, however, uses a wide variety of different measured variables, called features, and classification systems to accomplish these tasks. The well-established field of human speech processing has developed a number of different techniques to perform many of the aforementioned bioacoustics tasks. Melfrequency cepstral coefficients (MFCCs) and perceptual linear prediction (PLP) coefficients are two popular feature sets. The hidden Markov model (HMM), a statistical model similar to a finite autonoma machine, is the most commonly used supervised classification model and is capable of modeling both temporal and spectral variations. This research designs a framework that applies models from human speech processing for bioacoustic analysis tasks. The development of the generalized perceptual linear prediction (gPLP) feature extraction model is one of the more important novel contributions of the framework. Perceptual information from the species under study can be incorporated into the gPLP feature extraction model to represent the vocalizations as the animals might perceive them. By including this perceptual information and modifying parameters of the HMM classification system, this framework can be applied to a wide range of species. The effectiveness of the framework is shown by analyzing African elephant and beluga whale vocalizations. The features extracted from the African elephant data are used as input to a supervised classification system and compared to results from traditional statistical tests. The gPLP features extracted from the beluga whale data are used in an unsupervised classification system and the results are compared to labels assigned by experts. The development of a framework from which to build animal vocalization classifiers will provide bioacoustics researchers with a consistent platform to analyze and classify vocalizations. A common framework will also allow studies to compare results across species and institutions. In addition, the use of automated classification techniques can speed analysis and uncover behavioral correlations not readily apparent using traditional techniques.
Automatic identification of bird species by their vocalization is studied in this paper. Bird sounds are represented with two different parametric representations: (i) the mel-cepstrum parameters and (ii) a set of low-level signal parameters, both of which have been found useful for bird species recognition. Recognition is performed in a decision tree with support vector machine (SVM) classifiers at each node that perform classification between two species. Recognition is tested with two sets of bird species whose recognition has been previously tested with alternative methods. Recognition results with the proposed method suggest better or equal performance when compared to existing reference methods.
In the field of neural network research a number of experiments
described seem to be in contradiction with the classical pattern
recognition or statistical estimation theory. The authors attempt to
give some experimental understanding why this could be possible by
showing that a large fraction of the parameters (the weights of neural
networks) are of less importance and do not need to be measured with
high accuracy. The remaining part is capable to implement the desired
classifier and because this is only a small fraction of the total number
of weights, the reported experiments seem to be more realistic from a
classical point of view
Deep learning with a convolutional neural network (CNN) has been proved to be very effective in feature extraction and representation of images. For image classification problems, this work aim at finding which classifier is more competitive based on high-level deep features of images. In this paper, we have discussed the nearest neighbor, support vector machines and extreme learning machines for image classification under deep convolutional activation feature representation. Specifically, we adopt the benchmark object recognition dataset from multiple sources with domain bias for evaluating different classifiers. The deep features of the object dataset are obtained by a well-trained CNN with five convolutional layers and three fully-connected layers on the challenging ImageNet. Experiments demonstrate that the ELMs outperform SVMs in cross-domain recognition tasks. In particular, state-of-the-art results are obtained by kernel ELM which outperforms SVMs with about 4 % of the average accuracy. The Features and MATLAB codes in this paper are available in http://www.escience.cn/people/lei/index.html.
Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
The economic and wellness impact of stress on production and health of chickens cannot be over-emphasized. There have been several attempts to measure stress response in animals. Conventional methods of measuring stress are not necessarily good indicators of welfare as they detect stress only after it has negatively impacted the chickens. In the light of the problems associated with stress detection, there is a need to non-invasively measure stress early enough to enable the farmer to remove the stressor before it adversely affects the chickens. This dissertation evaluated the use of chicken vocalizations as an indicator of stress in chickens. ^ The vocalizations of chickens of varied ages, breeds and sex were recorded and analyzed using the modified Hidden Markov Model (HMM). The living conditions of the birds were altered and resultant vocalizations were analyzed in context. Control environments were also created and vocal recordings were made to compare with the stressful vocalizations. Analysis of chicken vocalizations using the HMM revealed that the model achieved an average classification accuracy of 74%. The HMM was also able to detect and distinguish between stressful and non-stressful vocalizations with 100% accuracy within 120 ms, making the model a good tool for early stress detection. ^ The acoustic characteristics of newly hatched chicks' vocalizations under natural and stressful conditions. Significant (P<0.0001) differences in the vocalization pattern of both groups were detected by Praat sound analysis software. To investigate the robustness of the HMM for stress detection, the effect of stress, age and diet on broiler chickens' vocalizations was determined. It was observed that vocalizations made by the older birds had a classification accuracy of greater than 90%. Using the intensity of chickens' vocalizations, habituation to human presence was determined in white leghorn birds. The intensity of the vocalizations of control hens was no longer significantly different from stressed birds after 3 week of exposure to human presence. Additionally, the hearing ability of chicken was determined using a comparative study of chickens' Auditory Brainstem Response (ABR) due to the effect of age, sex and production type. It was seen that in chickens, the peaks occurred later in younger birds than older ones. Sex and production type did not have any effect on the latency-intensity curve of the birds. In conclusion, this dissertation demonstrates the use of chicken vocalizations as a reliable indicator of stress in chickens and its potential use in early stress detection.
We developed an audio signal processing algorithm that detects rales (gurgling noises that are a distinct symptom of common respiratory diseases in poultry). We derived features from the audio by calculating mel frequency cepstral coefficients (MFCCs), clustering the MFCC vectors, and examining the distribution of cluster indices over a window of time. The features are classified with a C4.5 decision tree. Our training data consisted of eight minutes of manually labeled audio selected from 25 days of continuous recording from a controlled study. The experiment group was challenged with the infectious bronchitis virus and became sick, while the control group remained healthy. We tested the algorithm on the entire dataset and obtained results that match the course of the disease. Algorithms such as this could be used to continuously monitor chickens in commercial poultry farms, providing an early warning system that could significantly reduce the costs incurred from disease.
This paper is an effort to help prevent broiler chicken mortality caused by stressful conditions. We assume a relation between broiler chicken vocalizations and stress; therefore, microphones were used to monitor a flock of birds over the course of their lifetime (approximately 65 days). A noise removal method based on spectral oversubtraction was developed to filter out the significant fan and heater noise and shown to be very effective. Then, a radar processing technique was employed to count the number of vocalizations. It was found that the number of vocalizations is an effective technique for detecting stressful conditions, easily classifying the gathered test cases using a threshold classifier with perfect accuracy. Therefore, we conclude that this system could easily be adapted into an effective, inexpensive poultry flock monitoring tool, and the methods developed here could be applied to other similar monitoring applications.
LIBSVM is a library for support vector machines (SVM). Its goal is to help users to easily use SVM as a tool. In this document, we present all its imple-mentation details. For the use of LIBSVM, the README file included in the package and the LIBSVM FAQ provide the information.
Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques,
neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural
networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor
computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by
other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden
layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with
those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning
speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning
mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.
KeywordsExtreme learning machine–Support vector machine–ELM kernel–ELM feature space–Ensemble–Incremental learning–Online sequential learning
In this paper we explore and discuss the learning and generalization characteristics of the random vector version of the Functional-link net and compare these with those attainable with the GDR algorithm. This is done for a well-behaved deterministic function and for real-world data. It seems that ‘overtraining’ occurs for stochastic mappings. Otherwise there is saturation of training.
Until now the use of acoustic bio-responses in bio-environment control as indicators of animal-condition is limited to human perception. Coughing is a frequent symptom of many respiratory diseases affecting the airways and lungs of humans and animals.Registration of coughs from different pigs in a controlled test chamber was done in order to analyse the acoustical signal. A new approach is presented to distinguish cough sounds from other sounds, such as grunts, metal clanging and background noise, using neural networks as the classification method. Other signals (such as grunts, metal clanging, etc.) could also be detected.The best performance was obtained with a hybrid classifier that classifies coughs and metal clanging separately from the rest, giving better results compared to a probabilistic neural network (PNN) alone. The hybrid classifier, which consists of a 2- and a 4-class PNN, gave high discrimination performance in the case of grunts, metal clanging and background noise (91·4, 63·9 and 82·6%, respectively) and a performance of (91·9%) for correct classification in the case of coughs.
Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Detecting symptoms of diseases in poultry through audio signal processing
- Anderson Carroll Brandon
- Daley David
- Harbert Wayne
- Simeon
- W-Jackwood Britton Douglas
- Mark