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Diagram of the sensors location in the H1 farm, microphone, CO 2 sensor, humidity, and temperature sensor.
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Poultry meat is the world’s primary source of animal protein due to low cost and is widely eaten at a global level. However, intensive production is required to supply the demand although it generates stress to animals and welfare problems, which have to be reduced or eradicated for the better health of birds. In this study, bird welfare is measure...
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Context 1
... 2 , temperature and humidity measurements were carried out every 15 min, the mortality of animals was obtained daily, and the average weight weekly. The CO 2 , temperature, and humidity network sensors (see Figure 3) were distributed through the room and all data were collected in a hard disk via a management software for the daily management of the farm. The animals' weight and mortality were manually obtained. ...
Citations
... They are capable of seeing UV-A light (315-400 nm) in addition to the visible spectrum of 400-750 nm. In addition to their adeptness at perceiving colors, chickens have a broader visual capability than previously understood (Ham and Osorio, 2007;Olsson et al., 2015). ...
... Shortly after hatching, chicks are capable of navigating around obstacles, detecting moving objects, and pecking objects with precision. They can also differentiate between shallow and deep surfaces (Ham and Osorio, 2007;Seifert et al., 2020). ...
... Chickens have the ability to produce sounds ranging from approximately 60 to 80 decibels (dB), with the actual output dependent on the context and the individual chicken (Donofre et al., 2020;Ginovart-Panisello et al., 2020;Hill et al., 2014;Nicol, 2015). This range of vocalizations plays a crucial role in their communication and social interactions. ...
The environment in which animals are kept must provide suitable conditions for their species. This includes ensuring that animals are healthy, well-fed, safe, able to exhibit species-specific behaviors, not experiencing fear or pain, and not under chronic or acute stress. Poultry welfare is achieved when birds are raised in environments that meet their physiological and ethological needs. Fear can significantly impact animal welfare. Chickens have been significantly altered by human artificial selection. Despite this, they exhibit reactivity towards humans and tend to avoid them. Poultry animals reared in environmentally controlled poultry houses and bred for superior productivity are more sensitive to fear factors and have lost their adaptability to a great extent. This study aimed to determine the effect of personnel clothing color on stress and fear in chickens in layer hen coops. The experiment involved 32-week-old laying hens of three different genotypes. A worker in the henhouse wore six respective different colors of workwear (dark blue, green, red, yellow, black, and white), and sound measurements were taken during this time. The results showed that the color of the worker's clothing influenced the sound intensity of the chickens (P<0.05). White clothing elicited the least reaction, whereas black and dark blue elicited the most. The other three colors showed similar reactions. In conclusion, workers in layer hen coops wearing dark clothing, such as dark blue and black, can induce stress and noise in the animals. Additionally, chickens showed similar reactions to green, red, and yellow colors, with white being the color around which they felt the most secure.
... The system was programmed to record 15 min files with a sampling rate of 22.05 kHz, which were archived with flac compression [15]. Similar recording systems have been implemented by the authors in previous studies [16][17][18] to characterize broiler calls in both hatcheries and farms, and to identify CO 2 -related acoustic indicators. ...
... • Centroid is a frequency feature that identifies the centre of mass of the spectrum [25]. Previous studies by the authors suggest a relationship between vocal frequency and feed intake in commercial broiler farms [16]. • Mel Frequency Cepstral Coefficients (MFCC) 1-13 [26] are widely used frequency features that indicate the amount of energy compressed in a frequency region using a Mel filter bank. ...
... Information encoded in animals' calls can be used to detect stress. In poultry, the most important features related to stress are the number of vocalizations per unit of time (vocal-Num) [24,41,42] and the centroid, indicating the center of mass of the call [16]. Figure 4 shows the evolution over time, starting from three days before vaccination, and the continuous monitoring of the animals up to eight days after vaccination. ...
Acoustic studies on poultry show that chicken vocalizations can be a real-time indicator of the health conditions of the birds and can improve animal welfare and farm management. In this study, hens vaccinated against infectious laryngotracheitis (ILT) were acoustically recorded for 3 days before vaccine administration (pre-reaction period) and also from vaccination onwards, with the first 5 days being identified as the “reaction period” and the 5 following days as “post reaction”. The raw audio was pre-processed to isolate hen calls and the 13 Mel-frequency cepstral coefficients; then, the spectral centroid and the number of vocalizations were extracted to build the acoustic dataset. The experiment was carried out on the same farm but in two different houses. The hens from one house were assigned to the control group, without administration of the anti-inflammatory product, and the other formed the treatment group. Both acoustic data sets were recorded and processed in the same way. The control group was used to acoustically model the animal reaction to the vaccine and we automatically detected the hens’ vaccine reactions and side effects through acoustics. From Scikit-Learn algorithms, Gaussian Naive Bayes was the best performing model, with a balanced accuracy of 80% for modeling the reactions and non-reactions caused by ILT in the control group. Furthermore, the importance of algorithm permutation highlighted that the centroid and MFCC4 were the most important features in acoustically detecting the ILT vaccine reaction. The fitted Gaussian Naive Bayes model allowed us to evaluate the treatment group to determine if the vocalizations after vaccine administration were detected as non-reactions, due to the anti-inflammatory product’s effectiveness. Of the sample, 99% of vocalizations were classified as non-reactions, due to the anti-inflammatory properties of the product, which reduced vaccine reactions and side effects. The non-invasive detection of hens’ responses to vaccination to prevent respiratory problems in hens described in this paper is an innovative method of measuring and detecting avian welfare.
... When additional equipment is working, noise levels can exceed 90 dB, a value identified as leading to increased levels of stress and fearfulness in hens (Campo et al., 2005); the recommended exposure limit for workers is 85-90 dB, depending on country (IOSH, 2022). In turn, the environmental noise measured outside (15-20 m from poultry buildings) ranges from 44 to 63 dB (BC, 1999b;Damasceno et al., 2018;Ginovart-Panisello et al., 2020). ...
Poultry farming is one of the most efficient animal husbandry methods and it provides nutritional security to a significant number of the world population. Using modern intensive farming techniques, global production has reached 133.4 mil. t in 2020, with a steady growth each year. Such intensive growth methods however lead to a significant environmental footprint. Waste materials such as poultry litter and manure can pose a serious threat to environmental and human health, and need to be managed properly. Poultry production and waste by-products are linked to NH3, N2O and CH4 emissions, and have an impact on global greenhouse gas emissions, as well as animal and human health. Litter and manure can contain pesticide residues, microorganisms, pathogens, pharmaceuticals (antibiotics), hormones, metals, macronutrients (at improper ratios) and other pollutants which can lead to air, soil and water contamination as well as formation of antimicrobial/multidrug resistant strains of pathogens. Dust emitted from intensive poultry production operations contains feather and skin fragments, faeces, feed particles, microorganisms and other pollutants, which can adversely impact poultry health as well as the health of farm workers and nearby inhabitants. Fastidious odours are another problem that can have an adverse impact on health and quality of life of workers and surrounding population. This study discusses the current knowledge on the impact of intensive poultry farming on environmental and human health, as well as taking a look at solutions for a sustainable future.
... In [29], several analysis are conducted with two different groups at the same time, comparing the vocalisations when the birds are fed and when they are not, focusing on the food dependency previously mentioned. Finally, in [30], the authors detail the acoustic analysis of the farm acoustic environment to obtain the Equivalent Level (L eq ), the mean Peak Frequency (PF), and the PF variation, every 30 min, in order to evaluate other iterations of the growing process in the future and compare its measurements at every stage of the growing process. ...
... The system recorded for 40 days with a sampling frequency of 22.05 kHz and 8 bits resolution splitting data into 10 min files with a flac compression [34]. Similar previous deployments have been implemented by the authors in [30,35] Simultaneously, a carbon dioxide sensor was located at the center of farm A at 0.8 m from the ground and samples were obtained at intervals of 15 min. Figure 2 shows the CO 2 values measured in this study. ...
The concentration of CO2 is relatively large in poultry farms and high accumulations of this gas reduce animal welfare. Good control of its concentration is crucial for the health of the animals. The vocalizations of the chickens can show their level of well-being linked to the presence of carbon dioxide. An audio recording system was implemented and audio raw data was processed to extract acoustical features from four cycles of forty days, three of them from the same farm. This research aims to find the most relevant acoustic features extracted from the broiler’s calls that are related to the CO2 concentration and that could help to automate procedures. The results are encouraging since MFCC 6, 9, 4 and 3 are the most important features that relate the vocalizations of the chickens to the gas concentration, furthermore there is a clear and more similar representativeness trend during birds’ life period from day 15 to day 40.
... Different frequency-based filters have been adopted to remove the ventilation noise at pre-processing stage [12,25,26]. However, the frequency of sound produced by fan operation in commercial broiler houses remains to be understood. ...
... However, the frequency of sound produced by fan operation in commercial broiler houses remains to be understood. In this study, we found that the upper frequency range of the fan in commercial broiler houses varied between 1069-1203 Hz, which is slightly higher than those reported (1000 Hz) in previous studies [13,25]. Furthermore, our results show that both lower and upper limits of fan sound frequency generally increased as the birds grew, probably due to the increased ventilation rate and air speed. ...
Audio data collected in commercial broiler houses are mixed sounds of different sources that contain useful information regarding bird health condition, bird behavior, and equipment operation. However, characterizations of the sounds of different sources in commercial broiler houses have not been well established. The objective of this study was, therefore, to determine the frequency ranges of six common sounds, including bird vocalization, fan, feed system, heater, wing flapping, and dustbathing, at bird ages of week 1 to 8 in a commercial Ross 708 broiler house. In addition, the frequencies of flapping (in wing flapping events, flaps/s) and scratching (during dustbathing, scratches/s) behaviors were examined through sound analysis. A microphone was installed in the middle of broiler house at the height of 40 cm above the back of birds to record audio data at a sampling frequency of 44,100 Hz. A top-view camera was installed to continuously monitor bird activities. Total of 85 min audio data were manually labeled and fed to MATLAB for analysis. The audio data were decomposed using Maximum Overlap Discrete Wavelet Transform (MODWT). Decompositions of the six concerned sound sources were then transformed with the Fast Fourier Transform (FFT) method to generate the single-sided amplitude spectrums. By fitting the amplitude spectrum of each sound source into a Gaussian regression model, its frequency range was determined as the span of the three standard deviations (99% CI) away from the mean. The behavioral frequencies were determined by examining the spectrograms of wing flapping and dustbathing sounds. They were calculated by dividing the number of movements by the time duration of complete behavioral events. The frequency ranges of bird vocalization changed from 2481 ± 191–4409 ± 136 Hz to 1058 ± 123–2501 ± 88 Hz as birds grew. For the sound of fan, the frequency range increased from 129 ± 36–1141 ± 50 Hz to 454 ± 86–1449 ± 75 Hz over the flock. The sound frequencies of feed system, heater, wing flapping and dustbathing varied from 0 Hz to over 18,000 Hz. The behavioral frequencies of wing flapping were continuously decreased from week 3 (17 ± 4 flaps/s) to week 8 (10 ± 1 flaps/s). For dustbathing, the behavioral frequencies decreased from 16 ± 2 scratches/s in week 3 to 11 ± 1 scratches/s in week 6. In conclusion, characterizing sounds of different sound sources in commercial broiler houses provides useful information for further advanced acoustic analysis that may assist farm management in continuous monitoring of animal health and behavior. It should be noted that this study was conducted with one flock in a commercial house. The generalization of the results remains to be explored.
... Sound analysis was found to be efficient in identifying stress, diseases, and behavioral changes in these animals. Additionally, this is a technique that can also be implemented in a closed commercial building such as barns or pens, rather than an open space farm [104]. ...
Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no ‘benchmarks’ or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal’s emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals’ compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities.
... More details about the farm and equipment description can be found in the former article of the same authors [16], which was devoted to the analysis of the first recorded cycle. ...
... This preliminary comparison results encourage us to study deeply the relationship between the several parameters measured in [16], in order to detail the time-evolution of the several metrics that have shown relevant for the birds welfare evaluation. ...
The poultry meat industry is one of the most efficient biological systems to transform cereal protein into high quality protein for human consumption at a low cost. However, to supply the increasing demand of white meat, intensive production is required whiche generates stress for the animals, which can be a major source of welfare problems. In this study, a comparative acoustic analysis of two entire production cycles of an intensive broiler Ross 308 poultry farm in the Mediterranean area has been performed. The following step to consolidate the analysis is to stablise a clear comparison among the performance of the indicators (Leq, Leq variation, Peak Frequency (PF) and PF variation) in the conditions of two different recording campaigns corresponding to summer and winter entire production cycles. The acoustic maps of PF, Leq and the related variations should be validated in an inter-campaign comparison, which may also arise the possibility of changes due to the season of the year.
The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics , socioeconomic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities , we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.
This Scientific Opinion considers the welfare of domestic fowl (Gallus gallus) related to the production of meat (broilers) and includes the keeping of day-old chicks, broiler breeders, and broiler chickens. Currently used husbandry systems in the EU are described. Overall, 19 highly relevant welfare consequences (WCs) were identified based on severity, duration and frequency of occurrence: 'bone lesions', 'cold stress', 'gastro-enteric disorders', 'group stress', 'handling stress', 'heat stress', 'isolation stress', 'inability to perform comfort behaviour', 'inability to perform exploratory or foraging behaviour', 'inability to avoid unwanted sexual behaviour', 'locomotory disorders', 'prolonged hunger', 'prolonged thirst', 'predation stress', 'restriction of movement', 'resting problems', 'sensory under- and overstimulation', 'soft tissue and integument damage' and 'umbilical disorders'. These WCs and their animal-based measures (ABMs) that can identify them are described in detail. A variety of hazards related to the different husbandry systems were identified as well as ABMs for assessing the different WCs. Measures to prevent or correct the hazards and/or mitigate each of the WCs are listed. Recommendations are provided on quantitative or qualitative criteria to answer specific questions on the welfare of broilers and related to genetic selection, temperature, feed and water restriction, use of cages, light, air quality and mutilations in breeders such as beak trimming, de-toeing and comb dubbing. In addition, minimal requirements (e.g. stocking density, group size, nests, provision of litter, perches and platforms, drinkers and feeders, of covered veranda and outdoor range) for an enclosure for keeping broiler chickens (fast-growing, slower-growing and broiler breeders) are recommended. Finally, 'total mortality', 'wounds', 'carcass condemnation' and 'footpad dermatitis' are proposed as indicators for monitoring at slaughter the welfare of broilers on-farm.
