【图像检测】基于K-mean和形态学算法实现叶子病虫害检测matlab源码

1 模型

蔬菜病虫害的预警通常依靠植保专家知识来进行,较少采用数学建模方法来进行定量分析.为此,利用部分已知类别的训练样本抽取其关联规则作为监督信息,结合非监督学习的K-mean聚类算法,建立蔬菜黄曲条跳甲的预警模型.半监督学习算法既能发挥有监督学习准确率高的优点,又能充分地利用无监督学习的灵活性,具有一定的研究意义和实际意义.通过对广东省蔬菜黄曲条跳甲数据实验表明,半监督学习算法预警准确率比同条件下K-mean聚类算法的准确率高出24.31%.

2 部分代码

function varargout = LeafDiseaseGradingSystemGUI(varargin)
% LeafDiseaseGradingSystemGUI MATLAB code for LeafDiseaseGradingSystemGUI.fig
%     LeafDiseaseGradingSystemGUI, by itself, creates a new LeafDiseaseGradingSystemGUI or raises the existing
%     singleton*.
%
%     H = LeafDiseaseGradingSystemGUI returns the handle to a new LeafDiseaseGradingSystemGUI or the handle to
%     the existing singleton*.
%
%     LeafDiseaseGradingSystemGUI('CALLBACK',hObject,eventData,handles,...) calls the local
%     function named CALLBACK in LeafDiseaseGradingSystemGUI.M with the given input arguments.
%
%     LeafDiseaseGradingSystemGUI('Property','Value',...) creates a new LeafDiseaseGradingSystemGUI or raises the
%     existing singleton*. Starting from the left, property value pairs are
%     applied to the LeafDiseaseGradingSystemGUI before LeafDiseaseGradingSystemGUI_OpeningFcn gets called. An
%     unrecognized property name or invalid value makes property application
%     stop. All inputs are passed to LeafDiseaseGradingSystemGUI_OpeningFcn via varargin.
%
%     *See LeafDiseaseGradingSystemGUI Options on GUIDE's Tools menu. Choose "LeafDiseaseGradingSystemGUI allows only one
%     instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help LeafDiseaseGradingSystemGUI

% Last Modified by GUIDE v2.5 20-Jan-2015 14:49:28

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                  'gui_Singleton',  gui_Singleton, ...
                  'gui_OpeningFcn', @LeafDiseaseGradingSystemGUI_OpeningFcn, ...
                  'gui_OutputFcn',  @LeafDiseaseGradingSystemGUI_OutputFcn, ...
                  'gui_LayoutFcn', [] , ...
                  'gui_Callback',   []);
if nargin && ischar(varargin{1})
   gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
  [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
   gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before LeafDiseaseGradingSystemGUI is made visible.
function LeafDiseaseGradingSystemGUI_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject   handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)
% varargin   command line arguments to LeafDiseaseGradingSystemGUI (see VARARGIN)
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);

Disease_Grading = readfis('Disease_Grading.fis');

handles.Disease_Grading = Disease_Grading;
guidata(hObject,handles);

% Choose default command line output for LeafDiseaseGradingSystemGUI
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes LeafDiseaseGradingSystemGUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = LeafDiseaseGradingSystemGUI_OutputFcn(hObject, eventdata, handles) 
% varargout cell array for returning output args (see VARARGOUT);
% hObject   handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
varargout{1} = handles.output;


% --- Executes on button press in select_image.
function select_image_Callback(hObject, eventdata, handles)
% hObject   handle to select_image (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)

      [File_Name, Path_Name] = uigetfile('PATHNAME');
      I = imread([Path_Name,File_Name]);
      imshow([Path_Name,File_Name], 'Parent', handles.axes1); title('Original Leaf Image', 'Parent', handles.axes1);
      
      %# store queryname, version 1
      handles.I = I;
      guidata(hObject,handles);
      



% --- Executes on button press in segmentation.
function segmentation_Callback(hObject, eventdata, handles)
% hObject   handle to segmentation (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)


end

% displaying different show_clusters objects %

I_cluster_1 = segmented_images{1};

I_cluster_2 = segmented_images{2};

I_cluster_3 = segmented_images{3};

I_cluster_4 = segmented_images{4};

I_cluster_5 = segmented_images{5};

imshow(I_cluster_1,'Parent', handles.axes2); title('Cluster 1');

handles.I_cluster_1 = I_cluster_1;
handles.I_cluster_2 = I_cluster_2;
handles.I_cluster_3 = I_cluster_3;
handles.I_cluster_4 = I_cluster_4;
handles.I_cluster_5 = I_cluster_5;

guidata(hObject,handles);


% --- Executes on button press in disease_grade.
function disease_grade_Callback(hObject, eventdata, handles)
% hObject   handle to disease_grade (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)

Disease_Grading = handles.Disease_Grading;

white_pixels_I = handles.white_pixels_I ;

white_pixels_I_selected = handles.white_pixels_I_selected ;

percentage_infected = (white_pixels_I_selected/white_pixels_I)*100;

grade = evalfis(percentage_infected,Disease_Grading);

figure();

plot(percentage_infected,grade,'g*');

legend('Percent - Grade of Disease');

title('Disease Grade Classification Using Fuzzy Logic');
xlabel('Percentage');
ylabel('Disease Grade');

% --- Executes on button press in binary_original.
function binary_original_Callback(hObject, eventdata, handles)
% hObject   handle to binary_original (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)

I = handles.I;

BW_I = im2bw(I,0.17);

white_pixels_I = sum(BW_I(:) == 1);

se = strel('disk',1);

closeBW = imclose(BW_I,se);

imshow(closeBW,'Parent', handles.axes2); title('Binary of Original Image');

handles.white_pixels_I = white_pixels_I;

guidata(hObject,handles);



% --- Executes on button press in binary_diseased.
function binary_diseased_Callback(hObject, eventdata, handles)
% hObject   handle to binary_diseased (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)

I_selected = handles.I_slected ;

BW_I_selected = im2bw(I_selected,0.17);

white_pixels_I_selected = sum(BW_I_selected(:) == 1);

se = strel('disk',5);

closeBW = imclose(BW_I_selected,se);

imshow(closeBW,'Parent', handles.axes2); title('Binary of Clustered Image');

handles.white_pixels_I_selected = white_pixels_I_selected;

guidata(hObject,handles);


% --- Executes on selection change in show_clusters.
function show_clusters_Callback(hObject, eventdata, handles)
% hObject   handle to show_clusters (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)

% Hints: contents = cellstr(get(hObject,'String')) returns show_clusters contents as cell array
%       contents{get(hObject,'Value')} returns selected item from show_clusters
I_cluster_1 = handles.I_cluster_1 ;
I_cluster_2 = handles.I_cluster_2 ;
I_cluster_3 = handles.I_cluster_3 ;
I_cluster_4 = handles.I_cluster_4 ;
I_cluster_5 = handles.I_cluster_5 ;

% Determine the selected data set.
str = get(hObject, 'String');
val = get(hObject,'Value');

% Set current data to the selected data set.
switch str{val};
case 'Cluster 1' % User selects peaks.   
   imshow(I_cluster_1,'Parent', handles.axes2); title('Cluster 1');
   I_selected = I_cluster_1;
case 'Cluster 2' % User selects membrane.
   imshow(I_cluster_2,'Parent', handles.axes2); title('Cluster 2');
   I_selected = I_cluster_2;
case 'Cluster 3' % User selects sinc.
   imshow(I_cluster_3,'Parent', handles.axes2); title('Cluster 3');
   I_selected = I_cluster_3;
case 'Cluster 4' % User selects sinc.
   imshow(I_cluster_4,'Parent', handles.axes2); title('Cluster 4');
   I_selected = I_cluster_4;
case 'Cluster 5' % User selects sinc.
   imshow(I_cluster_5,'Parent', handles.axes2); title('Cluster 5');
   I_selected = I_cluster_5;
end

% Save the handles structure.

handles.I_slected = I_selected;

guidata(hObject,handles);



% --- Executes during object creation, after setting all properties.
function show_clusters_CreateFcn(hObject, eventdata, handles)
% hObject   handle to show_clusters (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   empty - handles not created until after all CreateFcns called

% Hint: popupmenu controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
   set(hObject,'BackgroundColor','white');
end

%closing dilation


% --- Executes on button press in save_image.
function save_image_Callback(hObject, eventdata, handles)
% hObject   handle to save_image (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles   structure with handles and user data (see GUIDATA)

axes2 = handles.axes2;

axes1 = handles.axes1;

h1=get(axes1,'Title');
h2=get(axes2,'Title');

figure();

subplot(1,2,1) ; imshow(getimage(axes1)); title(h1.String);
subplot(1,2,2) ; imshow(getimage(axes2)); title(h2.String);

3 仿真结果

4 参考文献

[1]王海超, 宗哲英, 张文霞, 殷晓飞, 王晓蓉, & 张海军等. (2019). 基于k均值聚类和环形结构提取算法的狭叶锦鸡儿木质部提取. 农业工程学报(1).​