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Automatic segmentation of plant leaves disease using min-max hue histogram and k-mean clustering

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Abstract

Automatic segmentation of plant image’s leaf diseases has recently become a popular area of study worldwide. The suggested approach automatically segments various areas of leaf disease from images of the plant, which can then be combined with machine learning or deep learning techniques to improve system accuracy. Our suggested method consists of three stages: Preprocessing is applied in the first stage where a rank order fuzzy (ROF) filter is proposed that reduces the background noise from the plant picture. In the next stage, disease spot detection is performed using proposed min-max hue histogram based techniques. Disease spot identification prior to segmentation helps in proper segmentation of k-mean clustering. The K-means clustering is then performed in the next stage to segment the leaf pictures into uniform regions. These segments are transformed into HSI color spaces and the segment with the largest hue value is extracted as the disease segment. The proposed methodology is implemented in Matlab 18a and studies are carried out on various plant images. The proposed ROF filter demonstrates superior results to the other state-of-the-art filters. The filter is also resistant to very large noise levels, and shows meaningful details at noise levels of 95%. Besides, our hue-based spot detection is compared with the existing method and it can be shown by the suggested approach, the diseases have been found mostly correctly. The segmentation accuracy of the proposed method is calculated using the Jaccard coefficient, Sensitivity and Positive Prediction Rate. Our proposed system achieved high Jaccard coefficient value of 0.7747.

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Abbreviations

F ij :

2D Index matrix value at ith row and jth coloum. The value is either 0 or 1

ρ :

The percentage of impulse noise prediction

P ij :

Pixel value at location (i, j)

M :

The total of rows in image

N :

The total of columns in image

D ′ :

First order absolute differences

W ij :

Window of size (2M × 1) (2M × 1) Centered at (i,j)

μ ij :

Fuzzy membership value

y ij :

Restoration term at location (i, j)

α :

A non-negative integer constant

H :

Hue component ranging from 0 to 360 degree

S :

Saturation component ranging from 0 to1

I :

Intensity component ranging from 0 to 1

θ :

Hue degree

H ij :

Unmodified hue value of the image at coordinate (i,j)

\( {H}_{ij}^{\prime } \) :

Modified hue value of the image at coordinate (i,j)

η :

Threshold to increase the hue value in the picture

τ1 and τ2 :

Threshold values in the hue histogram between 0 and 60 degree

C k :

Centroid of cluster k

d i, k :

the Euclidean distance between the center k and each data point i of an image

E(x):

The mean of the image x

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Authors and Affiliations

  1. DoCSE ,UIT-RGPV, Bhopal, India

    Vijay Kumar Trivedi & Piyush Kumar Shukla

  2. SoIT, UIT-RGPV, Bhopal, India

    Anjana Pandey

Authors
  1. Vijay Kumar Trivedi
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  2. Piyush Kumar Shukla
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  3. Anjana Pandey
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Correspondence to Vijay Kumar Trivedi.

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Trivedi, V.K., Shukla, P.K. & Pandey, A. Automatic segmentation of plant leaves disease using min-max hue histogram and k-mean clustering. Multimed Tools Appl 81, 20201–20228 (2022). https://doi.org/10.1007/s11042-022-12518-7

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