Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Multi-Pig Part Detection and Association with a Fully-Convolutional Network

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download

Pixel-Level Deep Multi-Dimensional Embeddings for Homogeneous Multiple Object Tracking

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, explicitly-defined post-processing steps. This work introduces two detection methods that incorporate multi-dimensional embeddings. We train deep CNNs to produce easily-clusterable embeddings for semantic instance segmentation and to enable object detection through pose estimation. The use of embeddings allows the method to identify per-pixel instance membership for both tasks. Our method specifically targets applications that require long-term tracking of homogeneous targets using a stationary camera. Furthermore, this method was developed and evaluated on a livestock tracking application which presents exceptional challenges that generalized tracking methods are not equipped to solve. This is largely because contemporary datasets for multiple object tracking lack properties that are specific to livestock environments. These include a high degree of visual similarity between targets, complex physical interactions, long-term inter-object occlusions, and a fixed-cardinality set of targets. For the reasons stated above, our method is developed and tested with the livestock application in mind and, specifically, group-housed pigs are evaluated in this work. Our method reliably detects pigs in a group housed environment based on the publicly available dataset with 99% precision and 95% using pose estimation and achieves 80% accuracy when using semantic instance segmentation at 50% IoU threshold. Results demonstrate our method\u27s ability to achieve consistent identification and tracking of group-housed livestock, even in cases where the targets are occluded and despite the fact that they lack uniquely identifying features. The pixel-level embeddings used by the proposed method are thoroughly evaluated in order to demonstrate their properties and behaviors when applied to real data. Adivser: Lance C. Pére

Application of deep learning for livestock behaviour recognition: a systematic literature review.

Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being the most commonly used. Occlusion and adhesion, data imbalance, and the complex livestock environment were the most prominent challenges reported by the primary studies. Finally, potential solutions and research directions were discussed in this SLR study to aid in developing autonomous livestock behaviour recognition systems

Panoptic Segmentation of Individual Pigs for Posture Recognition

Behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Systems based on computer vision in particular have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown excellent results. Object and keypoint detector have frequently been used to detect individual animals. Despite promising results, bounding boxes and sparse keypoints do not trace the contours of the animals, resulting in a lot of information being lost. Therefore, this paper follows the relatively new approach of panoptic segmentation and aims at the pixel accurate segmentation of individual pigs. A framework consisting of a neural network for semantic segmentation as well as different network heads and postprocessing methods will be discussed. The method was tested on a data set of 1000 hand-labeled images created specifically for this experiment and achieves detection rates of around 95% (F1 score) despite disturbances such as occlusions and dirty lenses

Application of deep learning for livestock behaviour recognition: a systematic literature review

Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being the most commonly used. Occlusion and adhesion, data imbalance, and the complex livestock environment were the most prominent challenges reported by the primary studies. Finally, potential solutions and research directions were discussed in this SLR study to aid in developing autonomous livestock behaviour recognition systems

Towards Intelligent Playful Environments for Animals based on Natural User Interfaces

Tesis por compendioEl estudio de la interacción de los animales con la tecnología y el desarrollo de sistemas tecnológicos centrados en el animal está ganando cada vez más atención desde la aparición del área de Animal Computer Interaction (ACI). ACI persigue mejorar el bienestar de los animales en diferentes entornos a través del desarrollo de tecnología adecuada para ellos siguiendo un enfoque centrado en el animal. Entre las líneas de investigación que ACI está explorando, ha habido bastante interés en la interacción de los animales con la tecnología basada en el juego. Las actividades de juego tecnológicas tienen el potencial de proveer estimulación mental y física a los animales en diferentes contextos, pudiendo ayudar a mejorar su bienestar. Mientras nos embarcamos en la era de la Internet de las Cosas, las actividades de juego tecnológicas actuales para animales todavía no han explorado el desarrollo de soluciones pervasivas que podrían proveerles de más adaptación a sus preferencias a la vez que ofrecer estímulos tecnológicos más variados. En su lugar, estas actividades están normalmente basadas en interacciones digitales en lugar de explorar dispositivos tangibles o aumentar las interacciones con otro tipo de estímulos. Además, estas actividades de juego están ya predefinidas y no cambian con el tiempo, y requieren que un humano provea el dispositivo o la tecnología al animal. Si los humanos pudiesen centrarse más en su participación como jugadores de un sistema interactivo para animales en lugar de estar pendientes de sujetar un dispositivo para el animal o de mantener el sistema ejecutándose, esto podría ayudar a crear lazos más fuertes entre especies y promover mejores relaciones con los animales. Asimismo, la estimulación mental y física de los animales son aspectos importantes que podrían fomentarse si los sistemas de juego diseñados para ellos pudieran ofrecer un variado rango de respuestas, adaptarse a los comportamientos del animal y evitar que se acostumbre al sistema y pierda el interés. Por tanto, esta tesis propone el diseño y desarrollo de entornos tecnológicos de juego basados en Interfaces Naturales de Usuario que puedan adaptarse y reaccionar a las interacciones naturales de los animales. Estos entornos pervasivos permitirían a los animales jugar por si mismos o con una persona, ofreciendo actividades de juego más dinámicas y atractivas capaces de adaptarse con el tiempo.L'estudi de la interacció dels animals amb la tecnologia i el desenvolupament de sistemes tecnològics centrats en l'animal està guanyant cada vegada més atenció des de l'aparició de l'àrea d'Animal Computer Interaction (ACI) . ACI persegueix millorar el benestar dels animals en diferents entorns a través del desenvolupament de tecnologia adequada per a ells amb un enfocament centrat en l'animal. Entre totes les línies d'investigació que ACI està explorant, hi ha hagut prou interès en la interacció dels animals amb la tecnologia basada en el joc. Les activitats de joc tecnològiques tenen el potencial de proveir estimulació mental i física als animals en diferents contextos, podent ajudar a millorar el seu benestar. Mentre ens embarquem en l'era de la Internet de les Coses, les activitats de joc tecnològiques actuals per a animals encara no han explorat el desenvolupament de solucions pervasives que podrien proveir-los de més adaptació a les seues preferències al mateix temps que oferir estímuls tecnològics més variats. En el seu lloc, estes activitats estan normalment basades en interaccions digitals en compte d'explorar dispositius tangibles o augmentar les interaccions amb estímuls de diferent tipus. A més, aquestes activitats de joc estan ja predefinides i no canvien amb el temps, mentre requereixen que un humà proveïsca el dispositiu o la tecnologia a l'animal. Si els humans pogueren centrar-se més en la seua participació com a jugadors actius d'un sistema interactiu per a animals en compte d'estar pendents de subjectar un dispositiu per a l'animal o de mantenir el sistema executant-se, açò podria ajudar a crear llaços més forts entre espècies i promoure millors relacions amb els animals. Així mateix, l'estimulació mental i física dels animals són aspectes importants que podrien fomentar-se si els sistemes de joc dissenyats per a ells pogueren oferir un rang variat de respostes, adaptar-se als comportaments de l'animal i evitar que aquest s'acostume al sistema i perda l'interès. Per tant, esta tesi proposa el disseny i desenvolupament d'entorns tecnològics de joc basats en Interfícies Naturals d'Usuari que puguen adaptar-se i reaccionar a les interaccions naturals dels animals. Aquestos escenaris pervasius podrien permetre als animals jugar per si mateixos o amb una persona, oferint activitats de joc més dinàmiques i atractives que siguen capaces d'adaptar-se amb el temps.The study of animals' interactions with technology and the development of animal-centered technological systems is gaining attention since the emergence of the research area of Animal Computer Interaction (ACI). ACI aims to improve animals' welfare and wellbeing in several scenarios by developing suitable technology for the animal following an animal-centered approach. Among all the research lines ACI is exploring, there has been significant interest in animals' playful interactions with technology. Technologically mediated playful activities have the potential to provide mental and physical stimulation for animals in different environmental contexts, which could in turn help to improve their wellbeing. As we embark in the era of the Internet of Things, current technological playful activities for animals have not yet explored the development of pervasive solutions that could provide animals with more adaptation to their preferences as well as offering varied technological stimuli. Instead, playful technology for animals is usually based on digital interactions rather than exploring tangible devices or augmenting the interactions with different stimuli. In addition, these playful activities are already predefined and do not change over time, while they require that a human has to be the one providing the device or technology to the animal. If humans could focus more on their participation as active players of an interactive system aimed for animals instead of being concerned about holding a device for the animal or keep the system running, this might help to create stronger bonds between species and foster better relationships with animals. Moreover, animals' mental and physical stimulation are important aspects that could be fostered if the playful systems designed for animals could offer a varied range of outputs, be tailored to the animal's behaviors and prevented the animal to get used to the system and lose interest. Therefore, this thesis proposes the design and development of technological playful environments based on Natural User Interfaces that could adapt and react to the animals' natural interactions. These pervasive scenarios would allow animals to play by themselves or with a human, providing more engaging and dynamic playful activities that are capable of adapting over time.Pons Tomás, P. (2018). Towards Intelligent Playful Environments for Animals based on Natural User Interfaces [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/113075TESISCompendi

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Tracking agonistic behaviors in pigs

Master of ScienceDepartment of Animal Sciences and IndustryLindsey E HulbertModern day animal production is intensively increasing to meet global demand for animal products. Producers must balance the increased demand for animal product and instill trust in consumers. Pigs raised in intensive production system display more fighting and unresolved conflict than wildtype pigs. This conflict is called “agonistic interactions”. These undesired behaviors occur mainly at the finishing stage of pigs when resources (water, food, space etc.) becomes limited or when animals meet unfamiliar pen mates. Chronic stress from unresolved conflict is an indication of poor animal welfare and may lead to reduced product quality. The first step in reducing the conflict is finding an efficient system to detect and track pigs at the individual level. Precision animal management is the incorporation of information technology into animal production to monitor animals online, which are supported with artificial intelligence to collect and analyze data that will help to sustainably improve livestock farming. While many systems exist, visual tracking has a great potential for commercial application because it is the least invasive. These systems will, therefore, be useful to producers by providing an early detection of agonistic behaviors in herd, provide timely intervention to compromised animals thereby increasing economic gains