Utveckling av metod för att bedöma behovet av ogräsbekämpning i spår

A system for weed management on railway embankments that is both adapted to the environment and efficient in terms of resources requires knowledge and understanding about the growing conditions of vegetation so that methods to control its growth can be adapted accordingly. Automated records could complement present-day manual inspections and over time come to replace these. One challenge is to devise a method that will result in a reasonable breakdown of gathered information that can be managed rationally by affected parties and, at the same time, serve as a basis for decisions with sufficient precision. The project examined two automated methods that may be useful for the Swedish Transport Administration in the future: 1) A machine vision method, which makes use of camera sensors as a way of sensing the environment in the visible and near infrared spectrum; and 2) An N-Sensor method, which transmits light within an area that is reflected by the chlorophyll in the plants. The amount of chlorophyll provides a value that can be correlated with the biomass. The choice of technique depends on how the information is to be used. If the purpose is to form a general picture of the growth of vegetation on railway embankments as a way to plan for maintenance measures, then the N-Sensor technique may be the right choice. If the plan is to form a general picture as well as monitor and survey current and exact vegetation status on the surface over time as a way to fight specific vegetation with the correct means, then the machine vision method is the better of the two. Both techniques involve registering data using GPS positioning. In the future, it will be possible to store this information in databases that are directly accessible to stakeholders online during or in conjunction with measures to deal with the vegetation. The two techniques were compared with manual (visual) estimations as to the levels of vegetation growth. The observers (raters) visual estimation of weed coverage (%) differed statistically from person to person. In terms of estimating the frequency (number) of woody plants (trees and bushes) in the test areas, the observers were generally in agreement. The same person is often consistent in his or her estimation: it is the comparison with the estimations of others that can lead to misleading results. The system for using the information about vegetation growth requires development. The threshold for the amount of weeds that can be tolerated in different track types is an important component in such a system. The classification system must be capable of dealing with the demands placed on it so as to ensure the quality of the track and other pre-conditions such as traffic levels, conditions pertaining to track location, and the characteristics of the vegetation. The project recommends that the Swedish Transport Administration: Discusses how threshold values for the growth of vegetation on railway embankments can be determined Carries out registration of the growth of vegetation over longer and a larger number of railway sections using one or more of the methods studied in the project Introduces a system that effectively matches the information about vegetation to its position Includes information about the growth of vegetation in the records that are currently maintained of the track’s technical quality, and link the data material to other maintenance-related databases Establishes a number of representative surfaces in which weed inventories (by measuring) are regularly conducted, as a means of developing an overview of the long-term development that can serve as a basis for more precise prognoses in terms of vegetation growth Ensures that necessary opportunities for education are put in place

Integration av webbaserat analysprogram och kartprogram : Integration of web based analysis-software andmap-software

I takt med att GIS (Grafiska InformationsSystem) blir allt vanligare och mer användarvänligt har WM-data sett att kunder skulle ha intresse i att kunna koppla information från sin verksamhet till en kartbild. Detta för att lättare kunna ta till sig informationen om hur den geografiskt finns utspridd över ett område för att t.ex. ordna effektivare tranporter. WM-data, som det här arbetet är utfört åt, avser att ta fram en prototyp som sedan kan visas upp för att påvisa för kunder och andra intressenter att detta är möjligt att genomföra genom att skapa en integration mellan redan befintliga system. I det här arbetet har prototypen tagits fram med skogsindustrin och dess lager som inriktning. Befintliga program som integrationen ska skapas mellan är båda webbaserade och körs i en webbläsare. Analysprogrammet som ska användas heter Insikt och är utvecklat av företaget Trimma, kartprogrammet heter GIMS som är WM-datas egna program. Det ska vara möjligt att i Insikt analysera data och skapa en rapport. Den ska sedan skickas till GIMS där informationen skrivs ut på kartan på den plats som respektive information hör till. Det ska även gå att välja ut ett eller flera områden i kartan och skicka till Insikt för att analysera information från enbart de utvalda områdena. En prototyp med önskad funktionalitet har under arbetets gång tagits fram, men för att ha en säljbar produkt är en del arbeta kvar. Prototypen har visats för ett antal intresserade som tyckte det var intressant och tror att det är något som skulle kunna användas flitigt inom många områden.

qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software

Background qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl. Findings Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. Conclusion qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.