Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean

by   Koushik Nagasubramanian, et al.

Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral bands that can distinguish between healthy and diseased specimens early in the growing season. Healthy and diseased hyperspectral data cubes were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383 to 1032 nm. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for identification of maximally effective band combinations. A binary classification between healthy and infected samples using six selected band combinations obtained a classification accuracy of 97 0.97 for the infected class. The results demonstrated that these carefully chosen bands are more informative than RGB images, and could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.



page 5

page 6

page 14


Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps

Our overarching goal is to develop an accurate and explainable model for...

In-field early disease recognition of potato late blight based on deep learning and proximal hyperspectral imaging

Effective early detection of potato late blight (PLB) is an essential as...

An automatic bad band preremoval algorithm for hyperspectral imagery

For most hyperspectral remote sensing applications, removing bad bands, ...

Leaf Image-based Plant Disease Identification using Color and Texture Features

Identification of plant disease is usually done through visual inspectio...

Hyperspectral Imaging to detect Age, Defects and Individual Nutrient Deficiency in Grapevine Leaves

Hyperspectral (HS) imaging was successfully employed in the 380 nm to 10...

Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images

In the small target detection problem a pattern to be located is on the ...

Ensemble Hyperspectral Band Selection for Detecting Nitrogen Status in Grape Leaves

The large data size and dimensionality of hyperspectral data demands com...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.