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

Gyr cows (Bos taurus indicus) in the peripartum period: assessment of calving prediction devices and factors affecting the maternal behavior and defensiveness

The aim of this study was to evaluate the potential use of the reticulo-rumen temperature and activity pattern of Gyr heifers as calving predictors and characterize the defensiveness and maternal care of primiparous and multiparous Gyr cows, evaluating the possible effects of parity and training protocol to the first milking prior calving toward these behaviors. Fifty-two Gir Leiteiro cows from the Empresa de Pesauissa Agropecuária de Minas Gerais (Epamig Oeste, Uberaba, Brazil) were used. All samples came from the same herd divided into three experimental groups, one for each chapter. (Chapter I): Forty pregnant Gir heifers received an intra-ruminal bolus that recorded reticulo-rumen temperature (Trr) and activity (Act). The animals had Trr and Act monitored during the prepartum period. We observed a decrease in Trr and an increase in Act in the days before calving. Differences in Trr and Act were most evident during the last 21 and 11 hours before parturition, respectively. There was a drop of 0.20°C in Trr. The analyzes revealed that both characteristics have the potential to predict parturition, however particularities in the thermal physiology of Zebu cattle must be considered when using devices validated only for European breeds. (Chapter II): Thirty-one Gir cows, among primiparous (n = 16) and multiparous (n = 15) were allocated in a maternity paddock monitored by video cameras. The behavior of the animals was collected in four periods: Pre-calving, Post-calving, First handling of calf and Post-handling. Primiparous cows showed a longer duration of standing with an arched spine and tended to move more than multiparous cows in the pre-calving period, which can be considered an indicator of pain and discomfort in these animals. Both primiparous and multiparous cows were protective of their calves, but only multiparous females were aggressive towards the handlers in the first calf handling. Furthermore, more protective cows spent more time eating before calving, while less attentive cows spent more time lying down before calving. (Chapter III): Thirty-seven primiparous dairy Gyr cows were allocated into two groups: Training Group (n = 16) was submitted to a protocol for the first milking, involving tactile stimulation; Control group (n = 21) was submitted to the common management of the farm, without interactions and/or additional handling. Animal behavior was recorded in three periods: Post-calving, First handling of calf and Post-handling. Calf latency to stand up, weight, and sex influenced cow-calf interactions, whereas training group cows touched less and spent more time not interacting with their calves. Both Training and Control groups had protective dams, but a higher percentage of Trained group dams were calmer toward calf handling. In conclusion, Trr and Act had potential to calving prediction in Gyr Heifers; Multiparous Gyr cows tended be more aggressive with their calves’ defense than primiparous; Training protocol to the first milking involving tactile stimulation reduced maternal care and defensiveness in primiparous Gyr cows.O objetivo desta tese foi avaliar o potencial uso dos padrões de temperatura retículo-ruminal e atividade de novilhas Gir como preditores de parto e caracterizar a defesa e cuidado materno de vacas Gir, avaliando os possíveis efeitos da paridade e do protocolo de treinamento para a primeira ordenha nestes comportamentos. Foram utilizadas cinquenta e duas vacas Gir Leiteiro da Empresa de Pesquisa Agropecuária d Minas Gerais (Epamig Oeste, Uberaba, Brasil). Todas as amostras foram oriundas de um mesmo rebanho dividido em três grupos experimentais, um para cada capítulo. (Capítulo I): Quarenta novilhas Gir prenhes receberam um bolus intra-ruminal que registrou a temperatura retículo-rúmen (Trr) e atividade (Act). Os animais tiveram Trr e Act monitorados durante o período pré-parto. Observamos diminuição do Trr e aumento do Act nos dias que antecederam o parto. As diferenças em Trr e Act foram mais evidentes durante as últimas 21 e 11 horas antes do parto, respectivamente. Houve queda de 0,20°C na Trr. As análises revelaram que ambas as características têm potencial para predizer o parto, porém particularidades na fisiologia térmica de bovinos zebuínos devem ser consideradas quando se utilizam dispositivos validados apenas para raças europeias. (Capítulo II): Trinta e uma vacas Gir, dentre primíparas (n = 16) e multíparas (n = 15) foram alocadas em um piquete de maternidade monitorado por câmeras de vídeo. Os comportamentos dos animais foram coletados em quatro períodos: Pré-parto, Pós-parto, Primeiro manejo do bezerro e Pós-manejo. As vacas primíparas apresentaram maior duração dos comportamentos de ficar em pé com a coluna arqueada e tenderam a se movimentar mais do que as multíparas no período pré-parto, o que pode ser considerado indicador de dor e desconforto nesses animais. Tanto as primíparas quanto as multíparas foram protetoras de seus bezerros, mas apenas as multíparas foram agressivas com os tratadores no primeiro manejo do bezerro. Além disso, vacas mais protetoras passaram mais tempo comendo antes do parto, enquanto vacas menos atentas passaram mais tempo deitadas antes do parto. (Capítulo III): Trinta e sete vacas Gir leiteiras primíparas foram alocadas em dois grupos: O Grupo Treinamento (n = 16) foi submetido a um protocolo para a primeira ordenha, envolvendo estimulação tátil; O Grupo de controle (n = 21) foi submetido ao manejo comum da fazenda, sem interações e/ou manejos adicionais. Os comportamentos dos animais foram registrados em três períodos: pós-parto, primeiro manejo do bezerro e pós-manejo. A latência do bezerro para se levantar, o peso e o sexo influenciaram as interações vaca-bezerro. Vacas do Grupo Treinamento tocaram menos e passaram mais tempo sem interagir com seus bezerros. Ambos os grupos de treinamento e controle tinham mães protetoras, mas uma porcentagem maior de mães do Grupo Treinamento foram mais calmas em relação ao manejo dos bezerros. Em conclusão, Trr e Act apresentaram potencial para predição de parto em Novilhas Gir; Vacas Gir multíparas tendem a ser mais agressivas na proteção de seus bezerros do que primíparas; Protocolo de treinamento para a primeira ordenha reduziu o cuidado e defesa materna nas vacas Gir primíparas

Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review

peer reviewedNumerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors

Behavioral fingerprinting: Acceleration sensors for identifying changes in livestock health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals

Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

AbstractLameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2×2×80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

PRECISION DAIRY FARMING TECHNOLOGY SOLUTIONS FOR DETECTING DAIRY COW DISEASE TO IMPROVE DAIRY COW WELL-BEING

Dairy cow health is multifactorial and complex. High producing dairy cows have been described as metabolic athletes, but metabolic and infectious diseases around calving affect many cows. These diseases have drastic negative effects on dairy cow well-being, milk production, and dairy farm economics. Early disease detection could potentially improve disease management, treatment, and future prevention techniques. The first objective of this research was to evaluate the use of activity, lying behavior, reticulorumen temperature, and rumination time determined by precision dairy farming technologies to detect transition cow diseases including hypocalcemia, ketosis, and metritis. The second objective was to evaluate the ability of activity, body weight, feeding behavior, lying behavior, milking order, milk yield and components, reticulorumen temperature, and rumination time determined by precision dairy farming technologies to predict clinical mastitis cases. The last objective of this research was to evaluate the precision dairy farming technologies used in Objective 3 to predict subclinical cases

Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data

peer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming

Artificial vision by thermography : calving prediction and defect detection in carbon fiber reinforced polymer

La vision par ordinateur est un domaine qui consiste à extraire ou identifier une ou plusieurs informations à partir d’une ou plusieurs images dans le but soit d’automatiser une tache, soit de fournir une aide à la décision. Avec l’augmentation de la capacité de calcul des ordinateurs, la vulgarisation et la diversification des moyens d’imagerie tant dans la vie quotidienne, que dans le milieu industriel,ce domaine a subi une évolution rapide lors de dernières décennies. Parmi les différentes modalités d’imagerie pour lesquels il est possible d’utiliser la vision artificielle cette thèse se concentre sur l’imagerie infrarouge. Plus particulièrement sur l’imagerie infrarouge pour les longueurs d’ondes comprises dans les bandes moyennes et longues. Cette thèse se porte sur deux applications industrielles radicalement différentes. Dans la première partie de cette thèse, nous présentons une application de la vision artificielle pour la détection du moment de vêlage en milieux industriel pour des vaches Holstein. Plus précisément l’objectif de cette recherche est de déterminer le moment de vêlage en n’utilisant que des données comportementales de l’animal. À cette fin, nous avons acquis des données en continu sur différents animaux pendant plusieurs mois. Parmi les nombreux défis présentés par cette application l’un d’entre eux concerne l’acquisition des données. En effet, les caméras que nous avons utilisées sont basées sur des capteurs bolométriques, lesquels sont sensibles à un grand nombre de variables. Ces variables peuvent être classées en quatre catégories : intrinsèque, environnemental, radiométrique et géométrique. Un autre défit important de cette recherche concerne le traitement des données. Outre le fait que les données acquises utilisent une dynamique plus élevée que les images naturelles ce qui complique le traitement des données ; l’identification de schéma récurrent dans les images et la reconnaissance automatique de ces derniers grâce à l’apprentissage automatique représente aussi un défi majeur. Nous avons proposé une solution à ce problème. Dans le reste de cette thèse nous nous sommes penchés sur la problématique de la détection de défaut dans les matériaux, en utilisant la technique de la thermographie pulsée. La thermographie pulsée est une méthode très populaire grâce à sa simplicité, la possibilité d’être utilisée avec un grand nombre de matériaux, ainsi que son faible coût. Néanmoins, cette méthode est connue pour produire des données bruitées. La cause principale de cette réputation vient des diverses sources de distorsion auquel les cameras thermiques sont sensibles. Dans cette thèse, nous avons choisi d’explorer deux axes. Le premier concerne l’amélioration des méthodes de traitement de données existantes. Dans le second axe, nous proposons plusieurs méthodes pour améliorer la détection de défauts. Chaque méthode est comparée à plusieurs méthodes constituant l’état de l’art du domaine.Abstract Computer vision is a field which consists in extracting or identifying one or more information from one or more images in order either to automate a task or to provide decision support. With the increase in the computing capacity of computers, the popularization and diversification of imaging means, both in industry, as well as in everyone’s life, this field has undergone a rapid development in recent decades. Among the different imaging modalities for which it is possible to use artificial vision, this thesis focuses on infrared imaging. More particularly on infrared imagery for wavelengths included in the medium and long bands. This thesis focuses on two radically different industrial applications. In the first part of this thesis, we present an application of artificial vision for the detection of the calving moment in industrial environments for Holstein cows. More precisely, the objective of this research is to determine the time of calving using only physiological data from the animal. To this end, we continuously acquired data on different animals over several days. Among the many challenges presented by this application, one of them concerns data acquisition. Indeed, the cameras we used are based on bolometric sensors, which are sensitive to a large number of variables. These variables can be classified into four categories: intrinsic, environmental, radiometric and geometric. Another important challenge in this research concerns the processing of data. Besides the fact that the acquired data uses a higher dynamic range than the natural images which complicates the processing of the data; Identifying recurring patterns in images and automatically recognizing them through machine learning is a major challenge. We have proposed a solution to this problem. In the rest of this thesis we have focused on the problem of defect detection in materials, using the technique of pulsed thermography. Pulse thermography is a very popular method due toits simplicity, the possibility of being used with a large number of materials, as well as its low cost. However, this method is known to produce noisy data. The main cause of this reputation comes from the various sources of distortion to which thermal cameras are sensitive. In this thesis, we have chosen to explore two axes. The first concerns the improvement of existing data processing methods. In the second axis, we propose several methods to improve fault detection. Each method is compared to several methods constituting the state of the art in the field

Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods.

peer reviewedThe behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification