Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Acquiring plant features with optical sensing devices in an organic strip-cropping system
    Krus, Anne ; Apeldoorn, Dirk Van; Valero, Constantino ; Ramirez, Juan José - \ 2020
    Agronomy 10 (2020)2. - ISSN 2073-4395
    Cabbages - Lidar - Plant extraction - Point cloud - Weighted sum

    The SUREVEG project focuses on improvement of biodiversity and soil fertility in organic agriculture through strip-cropping systems. To counter the additional workforce a robotic tool is proposed. Within the project, a modular proof of concept (POC) version will be produced that will combine detection technologies with actuation on a single-plant level in the form of a robotic arm. This article focuses on the detection of crop characteristics through point clouds obtained with two lidars. Segregation in soil and plants was successfully achieved without the use of additional data from other sensor types, by calculating weighted sums, resulting in a dynamically obtained threshold criterion. This method was able to extract the vegetation from the point cloud in strips with varying vegetation coverage and sizes. The resulting vegetation clouds were compared to drone imagery, to prove they perfectly matched all green areas in said image. By dividing the remaining clouds of overlapping plants by means of the nominal planting distance, the number of plants, their volumes, and thereby the expected yields per row could be determined.

    Extrapolation of in situ data from 1-km squares to adjacent squares using remote sensed imagery and airborne lidar data for the assessment of habitat diversity and extent
    Lang, Mait ; Vain, R. ; Bunce, R.G.H. ; Jongman, R.H.G. - \ 2015
    Environmental Monitoring and Assessment 187 (2015). - ISSN 0167-6369 - 16 p.
    Plant life forms - General habitat categories - Lidar - Landsat-7 Enhanced ThematicMapper Plus - Iterative self organising clustering - Maximumlikelihood
    Habitat surveillance and subsequent monitoring
    at a national level is usually carried out by recording
    data from in situ sample sites located according to
    predefined strata. This paper describes the application
    of remote sensing to the extension of such field data
    recorded in 1-km squares to adjacent squares, in order to
    increase sample number without further field visits.
    Habitats were mapped in eight central squares in northeast
    Estonia in 2010 using a standardized recording
    procedure. Around one of the squares, a special study
    site was established which consisted of the central
    square and eight surrounding squares. A Landsat-7
    Enhanced Thematic Mapper Plus (ETM+) image was
    used for correlation with in situ data. An airborne light
    detection and ranging (lidar) vegetation height map was
    also included in the classification. A series of tests were
    carried out by including the lidar data and contrasting
    analytical techniques, which are described in detail in
    the paper. Training accuracy in the central square varied
    from 75 to 100 %. In the extrapolation procedure to the
    surrounding squares, accuracy varied from 53.1 to
    63.1 %, which improved by 10 % with the inclusion
    of lidar data. The reasons for this relatively low classification
    accuracy were mainly inherent variability in the
    spectral signatures of habitats but also differences between
    the dates of imagery acquisition and field sampling.
    Improvements could therefore be made by better
    synchronization of the field survey and image acquisition
    as well as by dividing general habitat categories
    (GHCs) into units which are more likely to have similar
    spectral signatures. However, the increase in the number
    of sample kilometre squares compensates for the loss of
    accuracy in the measurements of individual squares.
    The methodology can be applied in other studies as
    the procedures used are readily available.
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