Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support precision beef cattle management. Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (\u3e12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry

One-dimensional vs. two-dimensional based features: Plant identification approach

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup

One-dimensional vs. two-dimensional based features: Plant identification approach

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup

Assessing the predictive value of dairy facial biometrics for measures of productivity, health, and social dominance

Includes bibliographical references.2018 Fall.To view the abstract, please see the full text of the document

The Prehistoric rock art of Morocco.

This study aims to examine all aspects of Moroccan rock art and place it in an archaeological and environmental context. Almost 300 sites are now known but few have been studied fully. This work is the first overall analysis to be attempted. Data on climatic changes during the Holocene period, together with archaeological and faunal reports, provided the necessary background to the rock art. The distribution of engraved and painted sites in Morocco is very uneven. Animals were the most frequent themes, but a review of all the sites revealed great site and subject diversity. Four main types of engravings were Identified, their Characteristics described and their distribution plotted. Climatic fluctuations, new animal species, the introduction of meth weapons, the chariot and writing established a chronological framework. A critical appraisal of these events led to a tentative chronology for Moroccan rock art, thought here to have started around 2500 be. The situation of rock art sites showed that they were chosen for very specific reasons, some of them by nomadic pastoralists. Viewing rock art as a medium of communication, it was proposed that the images were messages defining territories, proclaiming ownership or commemorating heroes or battles. The images may have two levels of meaning: one easily understood by members of the group and by outsiders, the second, symbolic, less obviously comprehensible. Moroccan rock art was not an isolated phenomenon in north Africa. The rock art of Algeria, Libya and Mauritania showed both similarities and differences, IrnpMng a cultural link, albeit tenuous, between these countries. Available archaeological, environmental and rock art data revealed striking differences In information-availability between north and south Morocco. Archaeological research has established a chronologicaal nd cultural framework, in northern Morocco,to which rock art adds nothing. On the other hand, rock engravings of metal weapons are almost the only evidence of a Moroccan Bronze Age. In southern Morocco, the distribution of rock art sites reveals intensive human activity in an area little known from excavation. Rock art, archaeology and environment are thus related in this study to producea comprehensive picture of the past

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

Linear discriminant analysis : a detailed tutorial

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a preprocessing step for machine learning and pattern classification applications. At the same time, it is usually used as a black box, but (sometimes) not well understood. The aim of this paper is to build a solid intuition for what is LDA, and how LDA works, thus enabling readers of all levels be able to get a better understanding of the LDA and to know how to apply this technique in different applications. The paper first gave the basic definitions and steps of how LDA technique works supported with visual explanations of these steps. Moreover, the two methods of computing the LDA space, i.e. class-dependent and class-independent methods, were explained in details. Then, in a step-by-step approach, two numerical examples are demonstrated to show how the LDA space can be calculated in case of the class-dependent and class-independent methods. Furthermore, two of the most common LDA problems (i.e. Small Sample Size (SSS) and non-linearity problems) were highlighted and illustrated, and state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted with different datasets to (1) investigate the effect of the eigenvectors that used in the LDA space on the robustness of the extracted feature for the classification accuracy, and (2) to show when the SSS problem occurs and how it can be addressed

Evaluating Adversarial Robustness of Detection-based Defenses against Adversarial Examples

Machine Learning algorithms provide astonishing performance in a wide range of tasks, including sensitive and critical applications. On the other hand, it has been shown that they are vulnerable to adversarial attacks, a set of techniques that violate the integrity, confidentiality, or availability of such systems. In particular, one of the most studied phenomena concerns adversarial examples, i.e., input samples that are carefully manipulated to alter the model output. In the last decade, the research community put a strong effort into this field, proposing new evasion attacks and methods to defend against them. With this thesis, we propose different approaches that can be applied to Deep Neural Networks to detect and reject adversarial examples that present an anomalous distribution with respect to training data. The first leverages the domain knowledge of the relationships among the considered classes integrated through a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the classifier is able to reject samples that violate the domain knowledge constraints. This approach can be applied in both single and multi-label classification settings. The second one is based on a Deep Neural Rejection (DNR) mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers. To this end, we exploit RBF SVM classifiers, which provide decreasing confidence values as samples move away from the training data distribution. Despite technical differences, this approach shares a common backbone structure with other proposed methods that we formalize in a unifying framework. As all of them require comparing input samples against an oversized number of reference prototypes, possibly at different representation layers, they suffer from the same drawback, i.e., high computational overhead and memory usage, that makes these approaches unusable in real applications. To overcome this limitation, we introduce FADER (Fast Adversarial Example Rejection), a technique for speeding up detection-based methods by employing RBF networks as detectors: by fixing the number of required prototypes, their runtime complexity can be controlled. All proposed methods are evaluated in both black-box and white-box settings, i.e., against an attacker unaware of the defense mechanism, and against an attacker who knows the defense and adapts the attack algorithm to bypass it, respectively. Our experimental evaluation shows that the proposed methods increase the robustness of the defended models and help detect adversarial examples effectively, especially when the attacker does not know the underlying detection system

Varieties of Deer Imagery: Gender and Cosmology in Prehistoric Belief Systems of Central Asia and South Siberia.

In the Altai Mountains of far-western Mongolia near the convergence of China, Kazakhstan, and Russia, the Biluut Rock Art Complex contains over 9,000 petroglyphs. Hundreds of associated archaeological features including stone mounds and different types of standing stones are also present. Perhaps most crucial to connecting Biluut rock art with these features are deer stones, found not only here, but also throughout northern and central Mongolia, the larger Altai region, and Central Asia. These monuments bear important connections to khirigsuurs, Bronze Age burial mounds of stone encircled by ringed “fences”. Stylized “Mongolian deer” engraved on the classical type of deer stone have also been found on natural rock panels on the three Biluut hills. Combined rock art and archaeological research as part of a larger project aims to clarify cultural developments in the Bronze and early Iron Ages, especially. My thesis contributes to these efforts by aiding in the documentation, cataloguing, analysis, and interpretation of Mongolian deer and other deer imagery at Biluut, in the context of rock art and archaeology in the general region. Systematic stylistic analysis of Mongolian deer forms is discussed in light of new data collected from Biluut 3 in the summer of 2011, then extended to a mixed body of deer imagery as a whole. This refines perspectives on the language of Mongolian deer imagery at Biluut and nearby sites (which I initiated as the focus of my McNair project in 2010-2011), and extends to the wider body of deer imagery. Considering the cosmology and gender significance of deer imagery, I critically engage with Jacobson-Tepfer’s “Deer Goddess” interpretation of the classical Mongolian deer figure-type. A core component of this research is an investigation of Mongolian shamanism and related Siberian forms of shamanism

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