Assessing the Activity of Individual Group-Housed Broilers Throughout Life Using a Passive Radio Frequency Identification System—A Validation Study

Sluis, M. van der; Haas, Y. de; Klerk, B. de; Rodenburg, T.B.; Ellen, E.D.

doi:10.3390/s20133612

Cited by 28 publications

(60 citation statements)

References 26 publications

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“…A per-animal approach in poultry is practically not possible, unlike in the cattle and swine industries. Passive radio-frequency systems function in a similar method to these infrared receivers and provide clear, individualised points of information on the animals within a flock, but they face the same issue of impracticality on large scales [40].…”

Section: Infrared Receiver Assessments Of Keel Bone Fractures In Layi...mentioning

confidence: 99%

Automated Tracking Systems for the Assessment of Farmed Poultry

Neethirajan¹

2022

Animals

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The world’s growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animals on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of artificial intelligence (AI)-assisted technology individualised, and per-herd assessments of livestock became possible and accurate. Various studies have shown that cameras linked with specialised systems of AI can properly analyse flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on recent advancements, this review explores the aspects of AI in the detection, counting, and tracking of poultry in commercial and research-based applications.

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Section: Infrared Receiver Assessments Of Keel Bone Fractures In Layi...mentioning

confidence: 99%

Automated Tracking Systems for the Assessment of Farmed Poultry

Neethirajan¹

2022

Animals

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“…1 Electronic Measurements and Signal Processing, University of Technology of Ilmenau, Helmholtzplatz 2, 98693 Ilmenau, Germany. 2 Military Institute of Engineering, Praça Gen Tibúrcio 80, Rio de Janeiro 22.290-270, Brazil.…”

Section: Authors' Contributionsmentioning

confidence: 99%

“…The localization problem in communication systems has been on research spot for many years. Node position estimation enables a large set of location-based services [1], boosts biological studies [2], and improves defense capabilities [3].…”

Section: Introductionmentioning

confidence: 99%

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Improving the performance of a radio-frequency localization system in adverse outdoor applications

Sousa

Santana

Fernandes

et al. 2021

J Wireless Com Network

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In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

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“…Each method has its advantages and disadvantages and can be employed depending on the purpose of monitoring. Some researchers [16,17,18] have used wearable sensors attached to the birds' legs to measure the activity and movement of birds, but for commercial settings, this is not feasible due to the technological limitations and high costs. Hence, optical flow-based video assessments would be ideal for monitoring of poultry behavior and physiology.…”

Section: Automatic Monitoring Surveillancementioning

confidence: 99%

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

Neethirajan¹

2021

Preprint

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<p>The automatic detection, counting and tracking of individual and flocked chickens in the poultry industry is of paramount to enhance farming productivity and animal welfare. Due to methodological difficulties, such as the complex background of images, varying lighting conditions, and occlusions from e.g., feeding stations, water nipple stations and barriers in the chicken rearing production floor, it is a challenging task to automatically recognize and track birds using computer software. Here, a deep learning model based on You Only Look Once (Yolov5) is proposed for detecting domesticated chickens from videos with varying complex backgrounds. A multiscale feature is being adapted to the Yolov5 network for mapping modules in the counting and tracking of the trajectories of the chickens. The Yolov5 network was trained and tested on our dataset which resulted in an enhanced tracking precision accuracy. Using Kalman Filter, the proposed model was able to track multiple chickens simultaneously with the focus to associate individual chickens across the frames of the video for real time and online applications. By being able to detect the chickens amid diverse background interference and counting them precisely along with tracking the movement and measuring their travelled path and direction, the proposed model provides excellent performance for on-farm applications. Artificial intelligence enabled automatic measurements of chicken behavior on-farm using cameras offers continuous monitoring of the chicken's ability to perch, walk, interact with other birds and the farm environment, as well as the assessment of dustbathing, thigmotaxis, and foraging frequency, which are important indicators for their ability to express natural behaviors. This study highlights the potential of automated monitoring of poultry through the usage of ChickTrack model as a digital tool in enabling science-based animal husbandry practices and thereby promote positive welfare for chickens in animal farming. </p>

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Assessing the Activity of Individual Group-Housed Broilers Throughout Life Using a Passive Radio Frequency Identification System—A Validation Study

Cited by 28 publications

References 26 publications

Automated Tracking Systems for the Assessment of Farmed Poultry

Automated Tracking Systems for the Assessment of Farmed Poultry

Improving the performance of a radio-frequency localization system in adverse outdoor applications

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

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