Visual identification of individual Holstein-Friesian cattle via deep metric learning

Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques. Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training. We propose the use of SoftMax-based reciprocal triplet loss to address the identification problem and evaluate the techniques in detail against fixed herd paradigms. We find that deep metric learning systems show strong performance even when many cattle unseen during system training are to be identified and re-identified - achieving 98.2% accuracy when trained on just half of the population. This work paves the way for facilitating the non-intrusive monitoring of cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary research such as behavioural analysis, disease outbreak tracing, and more. Key parts of the source code, network weights and underpinning datasets are available publicly.Comment: 37 pages, 14 figures, 2 tables; Submitted to Computers and Electronics in Agriculture; Source code and network weights available at https://github.com/CWOA/MetricLearningIdentification; OpenCows2020 dataset available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml1

Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South African using remote sensing techniques

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016.Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-

Machine Learning Methods for Autonomous Object Recognition and Restoration in Images

Image recognition and image restoration are important tasks in the field of image processing. Image recognition are becoming very popular due to the state-of-the-art deep learning methods. However, these models usually require big datasets and high computational costs, which could be challenging. This thesis proposes an online learning framework that deals with both small and big datasets. For small datasets, a Cauchy prior logistic regression classifier is proposed to provide a quick convergence, and the online weight updating scheme is efficient due to the previously trained weights being reused. For big datasets, convolutional neural network could be implemented. For image recognition, non-parametric classifiers are often used for image recognition such as K-nearest neighbours, however, K-nearest neighbours are vulnerable to noise and high dimensional features. This thesis proposes a non-parametric classifier based on Bayesian compressive sensing; the developed classifier is robust and it does not need a training stage. For image restoration, which is usually performed before image recognition as a preprocessing process. This thesis proposes such a joint framework that performs image recognition and restoration simultaneously. In image restoration, image rotation and occlusion are common problems but convolutional neural networks are not suitable to solve these due to the limitation of the convolutional process and pooling process. This thesis develops a joint framework based on capsule networks. The developed joint capsule framework could achieve a good result on recognition, image de-noising, recovering rotation and removing occlusion. The developed algorithms have been evaluated for vehicle logo restoration and recognition, however, they are transferable to other implementations. This thesis also developed an automatic detection and recognition framework for badger monitoring for the first time. Badger plays a key role in the transmission of bovine tuberculosis, which is described by government as the most pressing animal health problem in the UK. An automatic badger monitoring system could help researcher to understand the transmission mechanisms and thereby to develop methods to deal with the transmission between species

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Revealed preference analysis of consumers' switching behaviour towards rice brands in Nigeria.

Doctor of Philosophy in Agricultural Economics. University of KwaZulu-Natal, Pietermaritzburg, 20178Abstract in PDF file

2020 Program and Abstracts for the Celebration of Student Scholarship

Program and Abstracts from the Celebration of Student Scholarship held in the Spring of 2020

Trademarks and Textual Data: A Broader Perspective on Innovation = Marques et données textuelles : Une perspective élargie sur l’innovation

Patente messen häufig technische Innovationen, während Handelsmarken Low-Tech und Dienstleistungen abdecken. In dieser Arbeit werden Textdaten von Marken untersucht, um verschiedene Rechte des geistigen Eigentums zu kombinieren. Textdaten ermöglichen zum Beispiel die Analyse großer Datenmengen, die Kombination verschiedener Quellen und datengestützte Erkenntnisse. Die Kombination von Handelsmarken und Patenten in den Bereichen Robotik (Hightech) und Schuhe (Lowtech) bietet eine breitere Abdeckung und Details zu Innovationen, die je nach Sektor variieren. Im Musikinstrumentensektor verdeutlichen Textdaten zu Marken, Patenten und Designs den laufenden technologischen Wandel. Patente beziehen sich auf Daten und Digitalisierungsthemen und werden von High-Tech-Firmen genutzt, während Handelsmarken die Signalverarbeitung und Videospiele von Spielfirmen abdecken. Designs fungieren als verbindendes Element. Eine Differenzierung zwischen Unternehmen und Tätigkeitsbereichen ist möglich. Zusammenfassend zeigt die These, dass die Integration von textuellen Markendaten die Innovationsabdeckung erweitert

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience