Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations

Setting the stage to tag "n" track: a guideline for implementing, validating and reporting a radio frequency identification system for monitoring resource visit behavior in poultry

Passive radio frequency identification (RFID) can advance poultry behavior research by enabling automated, individualized, longitudinal, in situ, and noninvasive monitoring; these features can usefully extend traditional approaches to animal behavior monitoring. Furthermore, since the technology can provide insight into the visiting patterns of tagged animals at functional resources (e.g., feeders), it can be used to investigate individuals' welfare, social position, and decision-making. However, the lack of guidelines that would facilitate implementing an RFID system for such investigations, describing it, and establishing its validity undermines this technology's potential for advancing poultry science. This paper aims to fill this gap by 1) providing a nontechnical overview of how RFID functions; 2) providing an overview of the practical applications of RFID technology in poultry sciences; 3) suggesting a roadmap for implementing an RFID system in poultry behavior research; 4) reviewing how validation studies of RFID systems have been done in farm animal behavior research, with a focus on terminologies and procedures for quantifying reliability and validity; and 5) suggesting a way to report on an RFID system deployed for animal behavior monitoring. This guideline is aimed mainly at animal scientists, RFID component manufacturers, and system integrators who wish to deploy RFID system as an automated tool for monitoring poultry behavior for research purposes. For such a particular application, it can complement indications in classic general standards (e.g., ISO/IEC 18000-63) and provide ideas for setting up, testing, and validating an RFID system and a standard for reporting on its adequacy and technical aspects.

Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools

Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust-bathing hens in an experimental aviary. In addition, two YOLO (You Only Look Once) models were compared. YOLOv4-tiny needed about 4.26 h to train for 6000 epochs, compared to about 23.2 h for the full models of YOLOv4. In validation, the performance of the two models in terms of precision, recall, harmonic mean of precision and recall, and mean average precision (mAP) did not differ, while the value of frame per second was lower in YOLOv4 compared to the tiny version (31.35 vs. 208.5). The mAP stands at about 94% for the classification of hens on the floor, while the classification of dust-bathing hens was poor (28.2% in the YOLOv4-tiny compared to 31.6% in YOLOv4). In conclusion, ML successfully identified laying hens on the floor, whereas other PLF tools must be tested for the classification of dust-bathing hens.

Automated Tracking Systems for the Assessment of Farmed Poultry

Simple Summary

With the advent of artificial intelligence, the poultry sector is gearing up to adopt and embrace sensor technologies to enhance the production and the welfare of birds. Automated tracking and tracing of poultry birds has several advantages in poultry farms: overcoming the subjectivity of human measurements, enhancing the ability to provide quality care for the birds during their life on the farm, providing the ability to predict events and thereby enabling timely interventions, and many more. However, the technologies behind automated tracking systems are not ripe due to the lags in algorithms and practical implementation issues. This mini review provides a brief critical assessment of the current and recent advancements of automated tracking systems in the poultry industry and offers an outlook on future directions.

Abstract

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.

A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

A Systematic Review of Precision Livestock Farming in the Poultry Sector: Is Technology Focussed on Improving Bird Welfare?

Simple Summary

Precision livestock farming (PLF) is the use of technology to help farmers monitor and manage their animals and their farm. This technology can help to improve animal welfare by enabling farmers to act as soon as any problem arises. However, the technology can also be used to increase production efficiency on the farm, which could be prioritised over the animals’ welfare. The aim of this study was to give an overview of PLF technology development in poultry farming, and to investigate whether improving welfare has been the main goal of PLF development. The results suggest that PLF development in poultry farming so far has focussed on improving animal health and welfare, more so than increasing production. However, despite the interest in PLF research for poultry farming across the world (especially in the USA, China and Belgium), most of the technology is still being developed (prototypes); only a few are available for farmers to buy and use. This means that future work should focus on making these technologies commercially available to farmers, so that systems developed to improve welfare can be used to improve the welfare of farmed birds in the real world.

Abstract

Precision livestock farming (PLF) systems have the potential to improve animal welfare through providing a continuous picture of welfare states in real time and enabling fast interventions that benefit the current flock. However, it remains unclear whether the goal of PLF development has been to improve welfare or increase production efficiency. The aims of this systematic literature review are to provide an overview of the current state of PLF in poultry farming and investigate whether the focus of PLF research has been to improve bird welfare. The study characteristics extracted from 264 peer-reviewed publications and conference proceedings suggest that poultry PLF has received increasing attention on a global scale, but is yet to become a widespread commercial reality. PLF development has most commonly focussed on broiler farming, followed by laying hens, and mainly involves the use of sensors (environmental and wearable) and cameras. More publications had animal health and welfare than production as either one of or the only goal, suggesting that PLF development so far has focussed on improving animal health and welfare. Future work should prioritise improving the rate of commercialisation of PLF systems, so that their potential to improve bird welfare might be realised.

Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking

Simple Summary

The European Cooperation in Science and Technology (COST) Action GroupHouseNet aims to provide synergy among scientists to prevent damaging behavior in group-housed pigs and laying hens. One goal of this network is to determine how genetic and genomic tools can be used to breed animals that are less likely to perform damaging behavior on their pen-mates. In this review, the focus is on feather-pecking behavior in laying hens. Reducing feather pecking in large groups of hens is a challenge, because it is difficult to identify and monitor individual birds. However, current developments in sensor technologies and animal breeding have the potential to identify individual animals, monitor individual behavior, and link this information back to the underlying genotype. We describe a combination of sensor technologies and “-omics” approaches that could be used to select against feather-pecking behavior in laying hens.

Abstract

Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of “-omics” approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior.

An RFID-Based Smart Nest Box: An Experimental Study of Laying Performance and Behavior of Individual Hens

This study designed a radio-frequency identification (RFID)-based Internet of Things (IoT) platform to create the core of a smart nest box. At the sensing level, we have deployed RFID-based sensors and egg detection sensors. A low-frequency RFID reader is installed in the bottom of the nest box and a foot ring RFID tag is worn on the leg of individual hens. The RFID-based sensors detect when a hen enters or exits the nest box. The egg-detection sensors are implemented with a resistance strain gauge pressure sensor, which weights the egg in the egg-collection tube. Thus, the smart nest box makes it possible to analyze the laying performance and behavior of individual hens. An evaluative experiment was performed using an enriched cage, a smart nest box, web camera, and monitoring console. The hens were allowed 14 days to become accustomed to the experimental environment before monitoring began. The proposed IoT platform makes it possible to analyze the egg yield of individual hens in real time, thereby enabling the replacement of hens with egg yield below a pre-defined level in order to meet the overall target egg yield rate. The results of this experiment demonstrate the efficacy of the proposed RFID-based smart nest box in monitoring the egg yield and laying behavior of individual hens.