Spatial prediction of weed intensities from exact count data and image-based estimates

Gilles Guillot; N. Lorén; Mats Rudemo

Spatial prediction of weed intensities from exact count data and image-based estimates
Journal article, 2009

Collecting weed exact counts in an agricultural field is easy but extremely time consuming. Image analysis algorithms for object extraction applied to pictures of agricultural fields may be used to estimate the weed content with a high resolution (about 1 m(2)), and pictures that are acquired at a large number of sites can be used to obtain maps of weed content over a whole field at a reasonably low cost. However, these image-based estimates are not perfect and acquiring exact weed counts also is highly useful both for assessing the accuracy of the image-based algorithms and for improving the estimates by use of the combined data. We propose and compare various models for image index and exact weed count and we use them to assess how such data should be combined to obtain reliable maps. The method is applied to a real data set from a 30-ha field. We show that using image estimates in addition to exact counts allows us to improve the accuracy of maps significantly. We also show that the relative performances of the methods depend on the size of the data set and on the specific methodology (full Bayes versus plug-in) that is implemented.

Approximate Cox process

plants

segmentation

robot

Spatial prediction

color

Image analysis

crops

Precision farming

Poisson regression

distributions

Model-based geostatistics

Gaussian random field

gaussian random-fields

Multivariate data

bayesian prediction

linear mixed models

Author

Gilles Guillot

University of Gothenburg

Chalmers, Mathematical Sciences

Other publications Research

N. Lorén

Mats Rudemo

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Other publications Research

Journal of the Royal Statistical Society. Series C: Applied Statistics

0035-9254 (ISSN) 1467-9876 (eISSN)

Vol. 58 525-542

Subject Categories

Probability Theory and Statistics

More information

Created

10/8/2017

Spatial prediction of weed intensities from exact count data and image-based estimates Journal article, 2009