Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    '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.

    We have a manual that explains all the features 

Current refinement(s):

  • help
  • print

    Print search results

  • export

    Export search results

  • alert
    We will mail you new results for this query: keywords==Perennial Ryegrass
Check title to add to marked list
Feature filtering and selection for dry matter estimation on perennial ryegrass: A case study of vegetation indices
Alckmin, G.T. ; Kooistra, L. ; Lucieer, A. ; Rawnsley, R. - \ 2019
In: ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. - ISPRS (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ) - p. 1827 - 1831.
Biomass - Collinearity - Dry Matter - Feature Selection - Machine Learning - Pasture - Perennial Ryegrass - Vegetation Indices

Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n Combining double low line 900), indicates that for this response variable (i.e. kg.DM.ha−1), more than 80% of indices present a high degree of collinearity (correlation > |0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error – RMSE Combining double low line 412.27 kg.DM.ha−1) and tolerable models (with a smaller number of features – 4 VIs and within 10% of the lowest RMSE.).

Check title to add to marked list

Show 20 50 100 records per page

 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.