On finite-time ruin probabilities for classical risk models

info:eu-repo/semantics/publishe

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain

Determination of the within and between flock prevalence and identification of risk factors for Salmonella infections in laying hen flocks housed in conventional and alternative systems

Salmonella outbreaks in humans are often linked with the consumption of contaminated eggs. Therefore a profound knowledge of the actual prevalence of Salmonella spp. in laying hens and the factors that influence the presence and persistence of Salmonella on a farm is of utmost importance. The housing of laying hens in conventional battery cages will be forbidden in the European Union (EU) from 2012 onwards. There is an urgent need to evaluate whether this move to alternative housing systems will influence the prevalence of Salmonella in laying hens. Therefore, a cross-sectional study was performed in 5 European countries (Belgium, Germany, Greece, Italy and Switzerland) to determine the between and within flock prevalence of hens shedding Salmonella and to investigate whether there is an effect of the housing type on Salmonella prevalence. In total 292 laying hen farms were sampled in the month prior to depopulation. An on-farm questionnaire was used to collect information on general management practices and specific characteristics of the sampled flock. Twenty-nine flocks were found positive for at least 1 Salmonella-serotype. In these flocks the within flock prevalence of shedding hens, determined by individual sampling of 40 hens, varied between 0% and 27.50%. A wide variety of serotypes was isolated with Salmonella Enteritidis as the most common. Housing in conventional battery cages, the absence of dry cleaning in between production rounds and sampling in winter turned out to be risk factors for the shedding of Salmonella Enteritidis or Typhimurium (P<0.05)