Detect.m

% Project Title: Pomegranate Leaf Disease Detection
while 1
clc
close all 
clear all
flag = 1.0;

flag = webread('https://things.ubidots.com/api/v1.6/devices/demo/rc/lv?token=A1E-**************************');
if flag == 1
    
%[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');
URL = 'http://abcdstuff.net/plantix/8.jpg';
%filename = 'pic.jpg';
filename = '8.jpg';
urlwrite(URL,filename,'get',{'term','urlwrite'});
pathname = 'E:\Leaf_Disease_Detection_code\';

I = imread([pathname,filename]);
I = imresize(I,[256,256]);
%figure, imshow(I); title('Query Leaf Image');

% Enhance Contrast
I = imadjust(I,stretchlim(I));
figure, imshow(I);title('Contrast Enhanced');

% Otsu Segmentation
I_Otsu = im2bw(I,graythresh(I));
% Conversion to HIS
I_HIS = rgb2hsi(I);

%% Extract Features

% 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);

% Classify the colors in a*b* colorspace using K means clustering.
% Since the image has 3 colors create 3 clusters.
% Measure the distance using Euclidean Distance Metric.
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);
%[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3);
% Label every pixel in tha image using results from K means
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 of clustering
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');
set(gcf, 'Position', get(0,'Screensize'));

% Feature Extraction
x = '2';%inputdlg('Enter the cluster no. containing the ROI only:');
i = str2double(x);
% Extract the features from the segmented image
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 = im2bw(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); 
%G = imoverlay(zero_image,seg_img,[1 0 0]);

cc = bwconncomp(seg_img,6);
diseasedata = regionprops(cc,'basic');
A1 = diseasedata.Area;
sprintf('Area of the disease affected region is : %g%',A1);

I_black = im2bw(I,graythresh(I));
kk = bwconncomp(I,6);
leafdata = regionprops(kk,'basic');
A2 = leafdata.Area;
sprintf(' Total leaf area is : %g%',A2);

%Affected_Area = 1-(A1/A2);
Affected_Area = (A1/A2);
if Affected_Area < 0.1
    Affected_Area = Affected_Area+0.15;
end
sprintf('Affected Area is: %g%%',(Affected_Area*100))

% Create the Gray Level Cooccurance Matrices (GLCMs)
glcms = graycomatrix(img);

% Derive Statistics from GLCM
stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
Mean = mean2(seg_img);
Standard_Deviation = std2(seg_img);
Entropy = entropy(seg_img);
RMS = mean2(rms(seg_img));
%Skewness = skewness(img)
Variance = mean2(var(double(seg_img)));
a = sum(double(seg_img(:)));
Smoothness = 1-(1/(1+a));
Kurtosis = kurtosis(double(seg_img(:)));
Skewness = skewness(double(seg_img(:)));
% Inverse Difference Movement
m = size(seg_img,1);
n = size(seg_img,2);
in_diff = 0;
for i = 1:m
    for j = 1:n
        temp = seg_img(i,j)./(1+(i-j).^2);
        in_diff = in_diff+temp;
    end
end
IDM = double(in_diff);
    
feat_disease = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];
%%
% Load All The Features
load('Training_Data.mat')

% 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 ');
    prediction='Alternaria Alternata';
    thingHTTPuploadDATA = strcat('https://api.thingspeak.com/apps/thinghttp/send_request?api_key=**********&text=',prediction);
    webread(thingHTTPuploadDATA);
elseif result == 1
    %helpdlg(' Anthracnose ');
    disp('Anthracnose');
    prediction='Alternaria Alternata';
    thingHTTPuploadDATA = strcat('https://api.thingspeak.com/apps/thinghttp/send_request?api_key=****************&text=',prediction);
    webread(thingHTTPuploadDATA);
elseif result == 2
    %helpdlg(' Bacterial Blight ');
    disp(' Bacterial Blight ');
    prediction='Bacterial Blight';
    thingHTTPuploadDATA = strcat('https://api.thingspeak.com/apps/thinghttp/send_request?api_key=**************&text=',prediction);
    webread(thingHTTPuploadDATA);
elseif result == 3
    %helpdlg(' Cercospora Leaf Spot ');
    disp('Cercospora Leaf Spot');
    prediction='Cercospora Leaf Spot';
    thingHTTPuploadDATA = strcat('https://api.thingspeak.com/apps/thinghttp/send_request?api_key=**********&text=',prediction);
    webread(thingHTTPuploadDATA);
elseif result == 4
    %helpdlg(' Healthy Leaf ');
    disp('Healthy Leaf ');
    prediction='Healthy Leaf';
    thingHTTPuploadDATA = strcat('https://api.thingspeak.com/apps/thinghttp/send_request?api_key=***************&text=',prediction);
    webread(thingHTTPuploadDATA);
end

%% Evaluate Accuracy
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 = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
classes = svmclassify(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 Linear Kernel with 500 iterations is: %g%%',Max_Accuracy)

else
    s='no data'
end
end