Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops
Joint Authors
Jurado-Expósito, Montserrat
López-Granados, Francisca
de Castro, Ana-Isabel
Gómez-Casero, María-Teresa
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-05-02
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Medicine
Information Technology and Computer Science
Abstract EN
In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops.
Field studies were conducted for four years at different locations in Spain.
We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum.
To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF).
Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years.
Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery.
Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.
American Psychological Association (APA)
de Castro, Ana-Isabel& Jurado-Expósito, Montserrat& Gómez-Casero, María-Teresa& López-Granados, Francisca. 2012. Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops. The Scientific World Journal،Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-486546
Modern Language Association (MLA)
de Castro, Ana-Isabel…[et al.]. Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops. The Scientific World Journal No. 2012 (2012), pp.1-11.
https://search.emarefa.net/detail/BIM-486546
American Medical Association (AMA)
de Castro, Ana-Isabel& Jurado-Expósito, Montserrat& Gómez-Casero, María-Teresa& López-Granados, Francisca. Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops. The Scientific World Journal. 2012. Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-486546
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486546