Hyper-spectral frequency selection for the classification of vegetation diseases
Hyper-spectral frequency selection for the classification of vegetation diseases
Samenvatting
Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc-
tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled
experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5%
is achieved for distinguishing two important potato-plant diseases.
Organisatie | NHL Stenden Hogeschool |
Afdeling | Academie Technology & Innovation |
Lectoraat | Lectoraat Computervision & Data Science |
Datum | 2017-05-17 |
Type | Artikel |
Taal | Engels |