Weight and volume estimation of poultry and products based on computer vision systems: a review

2022

Animals

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.

Section: Need For Automated Poultry Surveillancementioning

confidence: 99%

Automated Tracking Systems for the Assessment of Farmed Poultry

2022

Animals

“…It is praised as a non-intrusive and non-invasive option for flock assessment that seriously reduces and even eliminates events of unnecessary stress that are commonly caused by human observation. This aspect makes it a beneficial tool for presenting a wide range of data on animals within a flock and for the sorting and grading of poultry-related products (Nyalala et al, 2021).…”

Section: Need For Automated Poultry Surveillancementioning

confidence: 99%

Automated Tracking Systems for the Assessment of Farmed Poultry

2021

Preprint

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 animal 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 individualized and per-herd assessments of livestock are possible and accurate. Various studies have shown cameras linked with specialized systems of AI can properly analyze flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on the recent advancements, this review explores the aspects of AI in the detection, counting and tracking of the poultry in commercial and research-based applications.

“…To address the growing sector of precision livestock farming, the development of automatic monitoring and surveillance systems for animal behavior and phenotyping, as well as real-time assessments using video and image analysis are gaining momentum. Temperature of the chicken body parts from thermal imaging [19], inter-individual interactions of chicken, movement [20], automated weighing of the animals to keep track of productivity [21], machine vision-based eggcounting systems are some ways by which phenotyping data can be automatically measured using monitoring systems. Automated monitoring, surveillance, and assessments are of immediate need for the poultry indicators such as individual tracking; genome wide association investigations for optimization of breeding; maintaining the individual animal identity; tracking the activity and space usage continuously; group level activity assessment; to be able to differentiate between individual animals; early detection of deviating patterns; social and behavioral problem detection in chickens; comparing the activities of current flock with past flocks; detect and count laying hens; analyzing the preference of light intensity of individuals and groups of hens and poultry birds; range use and fearfulness in free-range hens; keel bone fracture assessment from activity using video monitoring.…”

Section: Automatic Monitoring Surveillancementioning

confidence: 99%

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

2021

Preprint

<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>