Sound Analysis to Predict the Growth of Turkeys

Abdel-Kafy, El-Sayed M.; Ibraheim, Samya E.; Af, Finzi; Youssef, S.F.; Behiry, Fatma Mohammed; Provolo, G.

doi:10.3390/ani10050866

Cited by 8 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee et al [ 34 ] use more acoustic parameters to automatically detect stress in laying hens. Abdel-Kafy et al [ 35 ] found a highly significant negative correlation between the peak frequency of vocalisations and the weight and age of turkeys. Du et al [ 36 ] also address stress in laying hens by means of their vocalisation analysis, with the final goal of assessing their thermal comfort condition.…”

Section: Related Workmentioning

confidence: 99%

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

Ginovart-Panisello

Alsina-Pagès

Iriondo

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Ginovart-Panisello

Alsina-Pagès

Iriondo

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Microphones installed in the farm environment can capture and discriminate vocalisations from many animals simultaneously, making them a powerful tool for monitoring affective state. Vocalisations have been demonstrated to convey information about affective states in a wide range of farm animals [8,71,72], including pigs [73], cows [74] and chickens [75]. Vocalizations are often involuntary, especially those indicating negative affective states, and so are good indicators of immediate emotional reactions in animals [9].…”

Section: Microphones-vocalisationsmentioning

confidence: 99%

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Neethirajan¹

2022

Animals

View full text Add to dashboard Cite

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.

show abstract

“…However, these models need to be updated, compared to genetic lines, and be applied to heat stress situations. New approaches are being used to predict growth such as the statistical analysis of turkeys' grunt sounds to predict growth ( Abdel-Kafy et al., 2020 ). These types of data sets can be valuable in the future to build prediction models or growth curves.…”

Section: Discussionmentioning

confidence: 99%

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

Flores

Grimes

2022

Poultry Science

View full text Add to dashboard Cite

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.

show abstract

Sound Analysis to Predict the Growth of Turkeys

Cited by 8 publications

References 28 publications

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

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

Affective State Recognition in Livestock—Artificial Intelligence Approaches

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

Contact Info

Product

Resources

About