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).
5 完整MATLAB代码与数据下载地址
见博客主页头条
