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

被引:8
|
作者
Guillot, Gilles [1 ,2 ,4 ]
Loren, Niklas [3 ]
Rudemo, Mats [4 ]
机构
[1] Univ Oslo, Dept Biol, Ctr Ecol & Evolutionary Synth, N-0316 Oslo, Norway
[2] AgroParisTech, Inst Natl Rech Agron, Paris, France
[3] Swedish Inst Food & Biotechnol, Gothenburg, Sweden
[4] Chalmers Univ Technol, S-41296 Gothenburg, Sweden
基金
瑞典研究理事会;
关键词
Approximate Cox process; Gaussian random field; Image analysis; Model-based geostatistics; Multivariate data; Poisson regression; Precision farming; Spatial prediction; BAYESIAN PREDICTION; COLOR; CROPS; DISTRIBUTIONS; SEGMENTATION; PLANTS; ROBOT;
D O I
10.1111/j.1467-9876.2009.00664.x
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:525 / 542
页数:18
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