Sound Analysis to Predict the Growth of Turkeys

Enhancing poultry health management through machine learning-based analysis of vocalization signals dataset

Population expansion and rising consumer demand for nutrient-dense meals have both contributed to an increase in the consumption of animal protein worldwide. A significant portion of the meat and eggs used for human consumption come from the poultry industry. Early diagnosis and warning of infectious illnesses in poultry are crucial for enhancing animal welfare and minimizing losses in the breeding and production systems for poultry. On the other hand, insufficient techniques for early diagnosis as well as infectious disease control in poultry farms occasionally fail to stop declining productivity and even widespread death. Individual physiological, physical, and behavioral symptoms in poultry, such as fever-induced increases in body temperature, abnormal vocalization due to respiratory conditions, and abnormal behavior due to pathogenic infections, frequently represent the health status of the animal. When birds have respiratory problems, they make strange noises like coughing and snoring. The work is geared towards compiling a dataset of chickens that were both healthy and unhealthy. 100 day-old poultry birds were purchased and split into two groups at the experimental site, the poultry research farm at Bowen University. For respiratory illnesses, the first group received treatment, whereas the second group did not. After that, the birds were separated and caged in a monitored environment. To eliminate extraneous sounds and background noise that might affect the analysis, microphones were set a reasonable distance away from the birds. The data was gathered using 24-bit samples at 96 kHz. For 65 days, three times per day (morning, afternoon, and night) of audio data were continually collected. Food and water are constantly provided to the birds during this time. During this time, the birds have constant access to food and water. After 30 days, the untreated group started to sound sick with respiratory issues. This information was also noted as being unhealthy. Chickens' audio signals were recorded, saved in MA4, and afterwards converted to WAV format. This dataset's creation is intended to aid in the design of smart technologies capable of early detection and monitoring of the status of birds in poultry farms in a continuous, noninvasive, and automated way.

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Simple Summary

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Individuality in houbara chick calls and its dynamics throughout ontogeny

In many taxa, breeding success depends heavily on reliable vocal recognition between parents and offspring. Although the acoustic basis of this recognition has been explored in several species, few studies have examined the evolution of acoustic cues to identity across development. Here, in a captive breeding program, we investigated for the first time the acoustic signals produced by North African houbara bustard Chlamydotis undulata undulata chicks. Two call types (contact and distress) were recorded from 15 chicks in 4 age classes. Acoustic analyses showed that the acoustic parameters of the calls varied systematically with age in both contact and distress calls. However, both call types remained highly stereotyped and individualized between chicks at every tested age, indicating that calls encode reliable information about individual identity throughout development, thus potentially enabling the mother to distinguish her own chicks through their development up to fledging. Playback experiments are now needed to verify such parent-chick recognition in houbara bustards and its efficiency across chick ontogeny.

Performance and processing yield comparisons of Large White male turkeys by genetic lines, sources, and seasonal rearing

Large White male turkey genetic lines (GL) comparison in performance and processing yields under the same conditions are rare in the literature. Two rearing experiments (EXP) were conducted to accomplish 2 objectives. The first objective was to test the effects of poult source and genetic lines on performance and processing yields. The second objective was to extract season and growth patterns when comparing both EXP common treatments. In EXP 1, male poults from 5 different sources were randomly assigned to 48 concrete: litter-covered floor pens. In EXP 2, male poults from 7 different genetic lines were randomly assigned to 48 concrete: litter-covered floor pens. For both EXP, the experimental design was a completely randomized block design with a one-factor arrangement. Both EXP were placed in the same house with the same management and nutrition in two separate seasons of the same year. Bird performance and carcass processing yield were analyzed in SAS 9.4 or JMP 15.1 in a mixed model. In EXP 1 no significant difference in BW or processing yield was observed. However, a similar GL from a commercial hatchery had an improved feed conversion ratio (FCR) over the same GL sourced directly from the genetic company hatchery. In EXP 2, statistical differences were observed in performance and breast meat yield depending on the GL. A season effect was observed when comparing the two EXP. Birds raised in the fall season had a 2 kg BW increase, on average, over their spring counterparts. This difference in BW can also be observed in a statistically higher breast meat yield by the birds raised in the fall over the ones raised in the spring. In conclusion, a comparison between GL resulted in effects due to genetic line, poult source, and rearing season on bird performance and carcass yield.

Acoustic Description of the Soundscape of a Real-Life Intensive Farm and Its Impact on Animal Welfare: A Preliminary Analysis of Farm Sounds and Bird Vocalisations

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 measured by certain indicators: CO2, temperature, humidity, weight, deaths, food, and water intake. Additionally, we approach an acoustic analysis of bird vocalisations as a possible metric to add to the aforementioned parameters. For this purpose, an acoustic recording and analysis of an entire production cycle of an intensive broiler Ross 308 poultry farm in the Mediterranean area was performed. The acoustic dataset generated was processed to obtain the Equivalent Level (Leq), the mean Peak Frequency (PF), and the PF variation, every 30 min. This acoustical analysis aims to evaluate the relation between traditional indicators (death, weight, and CO2) as well as acoustical metrics (equivalent level impact (Leq) and Peak Frequency) of a complete intensive production cycle. As a result, relation between CO2 and humidity versus Leq was found, as well as decreases in vocalisation when the intake of food and water was large.