leaf disease classification using svm

Error in Detect (line 94)

test = feat_disease;

code

clc

close all

%quick way to reset

[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');

I = imread([pathname,filename]);

I = imresize(I,[256,256]);

figure, imshow(I); title('Query Leaf Image');

%% Image Cropping

I=imcrop(I);

figure, imshow(I); title('Image Cropping');

%% Enhance Contrast

I = imadjust(I,stretchlim(I));

figure, imshow(I);title('Contrast Enhanced');

%% Otsu Segmentation

I_Otsu = imbinarize(I,graythresh(I));

%% Conversion to HIS

I_HIS = rgb2hsi(I);

%% Extract Features^M

% Function call to evaluate features

%[feat_disease seg_img] = EvaluateFeatures(I)

% Color Image Segmentation

% Use of K Means clustering for segmentation

% Convert Image from RGB Color Space to L*a*b* Color Space

% The L*a*b* space consists of a luminosity layer 'L*', chromaticity-layer 'a*' and 'b*'.

% All of the color information is in the 'a*' and 'b*' layers.

cform = makecform('srgb2lab');

%% Apply the colorform

lab_he = applycform(I,cform);

ab = double(lab_he(:,:,2:3));

nrows = size(ab,1);

ncols = size(ab,2);

ab = reshape(ab,nrows*ncols,2);

nColors = 3;

[cluster_idx, cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...

'Replicates',3);

pixel_labels = reshape(cluster_idx,nrows,ncols);

figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');

%% Create a blank cell array to store the results.

segmented_images = cell(1,3);

%% Create RGB label using pixel_labels

rgb_label = repmat(pixel_labels,[1,1,3]);

for k = 1:nColors

colors = I;

colors(rgb_label ~= k) = 0;

segmented_images{k} = colors;

end

figure,

subplot(3,1,1);imshow(segmented_images{1});title('Cluster 1'); subplot(3,1,2);imshow(segmented_images{2});title('Cluster 2');

subplot(3,1,3);imshow(segmented_images{3});title('Cluster 3');

% % Feature Extraction

x = inputdlg('Enter the cluster no. containing the ROI only:');

i = str2double(x);

seg_img = segmented_images{i};

% Convert to grayscale if image is RGB

if ndims(seg_img) == 3

img = rgb2gray(seg_img);

end

figure, imshow(img); title('Gray Scale Image');

%% Evaluate the disease affected area

black = imbinarize(seg_img,graythresh(seg_img));

% figure, imshow(black);title('Black & White Image');

m = size(seg_img,1);

n = size(seg_img,2);

zero_image = zeros(m,n);

cc = bwconncomp(seg_img,6);

diseasedata = regionprops(cc,'basic');

A1 = diseasedata.Area;

sprintf('Area of the disease affected region is : %g%',A1)

I_black = imbinarize(I,graythresh(I));

kk = bwconncomp(I,6);

leafdata = regionprops(kk,'basic');

A2 = leafdata.Area;

sprintf(' Total leaf area is : %g%',A2)

Affected_Area = (A1/A2);

if Affected_Area < 1

Affected_Area = Affected_Area+0.15;

end

sprintf('Affected Area is: %g%%',(Affected_Area*100))

%%

% Load All The Features

% load('Training_Data.mat')

sprintf('train data');

% Put the test features into variable 'test'

test = feat_disease;

result = multisvm(Train_Feat,Train_Label,test);

%disp(result);

% Visualize Results

if result == 0

helpdlg(' Alternaria Alternata ');

disp(' Alternaria Alternata ');

elseif result == 1

helpdlg(' Anthracnose ');

disp('Anthracnose');

elseif result == 2

helpdlg(' Bacterial Blight ');

disp(' Bacterial Blight ');

elseif result == 3

helpdlg(' Cercospora Leaf Spot ');

disp('Cercospora Leaf Spot');

elseif result == 4

helpdlg(' Healthy Leaf ');

disp('Healthy Leaf ');

end

%% Evaluate Accuracy^M

load('Accuracy_Data.mat')

Accuracy_Percent= zeros(200,1);

for i = 1:500

data = Train_Feat;

%groups = ismember(Train_Label,1);

groups = ismember(Train_Label,0);

[train,test] = crossvalind('HoldOut',groups);

cp = classperf(groups);

svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

classes = ClassificationSVM(svmStruct,data(test,:),'showplot',false);

classperf(cp,classes,test);

Accuracy = cp.CorrectRate;

Accuracy_Percent(i) = Accuracy.*100;

end

Max_Accuracy = max(Accuracy_Percent);

sprintf('Accuracy of classifier after 500 iterations is: %g%%',Max_Accuracy)

[KALYAN ACHARJYA]

Please provide the requred data file to check the code?

Answer 1

[kumari p]

clc

close all

%quick way to reset

[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');

I = imread([pathname,filename]);

I = imresize(I,[256,256]);

figure, imshow(I); title('Query Leaf Image');

%% Image Cropping

I=imcrop(I);

figure, imshow(I); title('Image Cropping');

%% Enhance Contrast

I = imadjust(I,stretchlim(I));

figure, imshow(I);title('Contrast Enhanced');

%% Otsu Segmentation

I_Otsu = imbinarize(I,graythresh(I));

%% Conversion to HIS

I_HIS = rgb2hsi(I);

%% Extract Features^M

% Function call to evaluate features

%[feat_disease seg_img] = EvaluateFeatures(I)

% Color Image Segmentation

% Use of K Means clustering for segmentation

% Convert Image from RGB Color Space to L*a*b* Color Space

% The L*a*b* space consists of a luminosity layer 'L*', chromaticity-layer 'a*' and 'b*'.

% All of the color information is in the 'a*' and 'b*' layers.

cform = makecform('srgb2lab');

%% Apply the colorform

lab_he = applycform(I,cform);

ab = double(lab_he(:,:,2:3));

nrows = size(ab,1);

ncols = size(ab,2);

ab = reshape(ab,nrows*ncols,2);

nColors = 3;

[cluster_idx, cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...

'Replicates',3);

pixel_labels = reshape(cluster_idx,nrows,ncols);

figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');

%% Create a blank cell array to store the results.

segmented_images = cell(1,3);

%% Create RGB label using pixel_labels

rgb_label = repmat(pixel_labels,[1,1,3]);

for k = 1:nColors

colors = I;

colors(rgb_label ~= k) = 0;

segmented_images{k} = colors;

end

figure,

subplot(3,1,1);imshow(segmented_images{1});title('Cluster 1'); subplot(3,1,2);imshow(segmented_images{2});title('Cluster 2');

subplot(3,1,3);imshow(segmented_images{3});title('Cluster 3');

% % Feature Extraction

x = inputdlg('Enter the cluster no. containing the ROI only:');

i = str2double(x);

seg_img = segmented_images{i};

% Convert to grayscale if image is RGB

if ndims(seg_img) == 3

img = rgb2gray(seg_img);

end

figure, imshow(img); title('Gray Scale Image');

%% Evaluate the disease affected area

black = imbinarize(seg_img,graythresh(seg_img));

% figure, imshow(black);title('Black & White Image');

m = size(seg_img,1);

n = size(seg_img,2);

zero_image = zeros(m,n);

cc = bwconncomp(seg_img,6);

diseasedata = regionprops(cc,'basic');

A1 = diseasedata.Area;

sprintf('Area of the disease affected region is : %g%',A1)

I_black = imbinarize(I,graythresh(I));

kk = bwconncomp(I,6);

leafdata = regionprops(kk,'basic');

A2 = leafdata.Area;

sprintf(' Total leaf area is : %g%',A2)

Affected_Area = (A1/A2);

if Affected_Area < 1

Affected_Area = Affected_Area+0.15;

end

sprintf('Affected Area is: %g%%',(Affected_Area*100))

%%

% Load All The Features

% load('Training_Data.mat')

sprintf('train data');

% Put the test features into variable 'test'

test = feat_disease;

result = multisvm(Train_Feat,Train_Label,test);

%disp(result);

% Visualize Results

if result == 0

helpdlg(' Alternaria Alternata ');

disp(' Alternaria Alternata ');

elseif result == 1

helpdlg(' Anthracnose ');

disp('Anthracnose');

elseif result == 2

helpdlg(' Bacterial Blight ');

disp(' Bacterial Blight ');

elseif result == 3

helpdlg(' Cercospora Leaf Spot ');

disp('Cercospora Leaf Spot');

elseif result == 4

helpdlg(' Healthy Leaf ');

disp('Healthy Leaf ');

end

%% Evaluate Accuracy^M

load('Accuracy_Data.mat')

Accuracy_Percent= zeros(200,1);

for i = 1:500

data = Train_Feat;

%groups = ismember(Train_Label,1);

groups = ismember(Train_Label,0);

[train,test] = crossvalind('HoldOut',groups);

cp = classperf(groups);

svmStruct = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');

classes = ClassificationSVM(svmStruct,data(test,:),'showplot',false);

classperf(cp,classes,test);

Accuracy = cp.CorrectRate;

Accuracy_Percent(i) = Accuracy.*100;

end

Max_Accuracy = max(Accuracy_Percent);

sprintf('Accuracy of classifier after 500 iterations is: %g%%',Max_Accuracy)