An image-processing based algorithm to automatically identify plant disease visual symptoms

This study describes an image-processing based method that identifies the visual symptoms of plant diseases, from an analysis of coloured images. The processing algorithm developed starts by converting the RGB image of the diseased plant or leaf, into the H, I3a and I3b colour transformations. The I...

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Bibliographic Details
Published in:Biosystems engineering Vol. 102; no. 1; pp. 9 - 21
Main Authors: Camargo, A., Smith, J.S.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 2009
Elsevier
Subjects:
Agricultural machinery and engineering
Agronomy. Soil science and plant productions
algorithms
automation
bananas
Biological and medical sciences
black Sigatoka
disease detection
Fundamental and applied biological sciences. Psychology
Generalities. Biometrics, experimentation. Remote sensing
image analysis
leaves
Phytopathology. Animal pests. Plant and forest protection
signs and symptoms (plants)
Bioengineering
Engineering
Symptomatology
Plant pathology
Image processing
Agriculture
Algorithm
ISSN:1537-5110, 1537-5129
Online Access:Get full text
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Summary:This study describes an image-processing based method that identifies the visual symptoms of plant diseases, from an analysis of coloured images. The processing algorithm developed starts by converting the RGB image of the diseased plant or leaf, into the H, I3a and I3b colour transformations. The I3a and I3b transformations are developed from a modification of the original I1I2I3 colour transformation to meet the requirements of the plant disease data set. The transformed image is then segmented by analysing the distribution of intensities in a histogram. Rather than using the traditional approach of selecting the local minimum as the threshold cut-off, the set of local maximums are located and the threshold cut-off value is determined according to their position in the histogram. This technique is particularly useful when the target in the image data set is one with a large distribution of intensities. In tests, once the image was segmented, the extracted region was post-processed to remove pixel regions not considered part of the target region. This procedure was accomplished by analysing the neighbourhood of each pixel and the gradient of change between them. To test the accuracy of the algorithm, manually segmented images were compared with those segmented automatically. Results showed that the developed algorithm was able to identify a diseased region even when that region was represented by a wide range of intensities.
Bibliography:http://dx.doi.org/10.1016/j.biosystemseng.2008.09.030
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ISSN:1537-5110
1537-5129
DOI:10.1016/j.biosystemseng.2008.09.030