【疾病分类】基于matlab GUI模糊逻辑分类叶病严重程度分级系统【含Matlab源码 194期】

一、模糊逻辑(Fuzzy Logic)简介

理论知识参考：模糊逻辑(Fuzzy Logic)

二、部分源代码

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});
endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
elsegui_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 1handles.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)I = handles.I;% Creating color transformation from sRGB to L*a*b %
cform = makecform('srgb2lab'); lab_I = applycform(I,cform);ab = double(lab_I(:,:,2:3));nrows = size(ab,1);
ncols = size(ab,2);ab = reshape(ab,nrows*ncols,2);
% No of clusters to be created with five iterations %
nColors =5;
[cluster_idx cluster_center] = kmeans(ab,nColors,'EmptyAction','singleton','distance','sqEuclidean','start',[128,128;128,128;128,128;128,128;128,128]); pixel_labels = reshape(cluster_idx,nrows,ncols); segmented_images = cell(5);rgb_label = repmat(pixel_labels,[1 1 3]);for k = 1:nColors
color = I;
color(rgb_label ~= k) = 0;
segmented_images{k} = color;
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);

三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.

【疾病分类】基于matlab GUI模糊逻辑分类叶病严重程度分级系统【含Matlab源码 194期】相关推荐

【疾病分类】模糊逻辑分类叶病严重程度分级系统【含GUI Matlab源码 194期】
⛄一.模糊逻辑(Fuzzy Logic)简介理论知识参考:模糊逻辑(Fuzzy Logic) ⛄二.部分源代码 function varargout = LeafDiseaseGradingSyst ...
【图像去噪】基于matlab GUI butterworth+中值+维纳+小波图像去噪【含Matlab源码 520期】
⛄一.获取代码方式获取代码方式1: 完整代码已上传我的资源:[图像去噪]基于matlab GUI butterworth+中值+维纳+小波图像去噪[含Matlab源码 520期] 获取代码方式2: ...
【物理应用】基于matlab GUI气象参数计算综合指标和IAQI【含Matlab源码 2116期】
⛄一.获取代码方式获取代码方式1: 完整代码已上传我的资源:[物理应用]基于matlab GUI气象参数计算综合指标和IAQI[含Matlab源码 2116期] 点击上面蓝色字体,直接付费下载,即可 ...
基于JAVA华水疫情预警与防护用品调度系统计算机毕业设计源码+数据库+lw文档+系统+部署
基于JAVA华水疫情预警与防护用品调度系统计算机毕业设计源码+数据库+lw文档+系统+部署基于JAVA华水疫情预警与防护用品调度系统计算机毕业设计源码+数据库+lw文档+系统+部署本源码技术栈: ...
【语音处理】基于matlab GUI低通滤波器语音信号加噪与去噪【含Matlab源码 1708期】
⛄一.语音处理简介(附课程作业报告) 1 语音信号的特点通过对大量语音信号的观察和分析发现,语音信号主要有下面两个特点: ①在频域内,语音信号的频谱分量主要集中在300-3400Hz的范围内.利用这 ...
基于Vue+Express+Mysql开发的手机端电影购票系统（附源码）
基于Vue+Express+Mysql开发的手机端电影购票系统基于手机的电影购票系统-Vue+Node 一个Vue+Express+Mysql的电影售票项目项目完整源码下载 https://dow ...
基于JavaWeb Mybatis+MVC(JSP + servlet + javabean)的高校就业管理系统(含项目源码)
基于JavaWeb Mybatis+MVC的高校就业管理系统项目简介基本功能简介项目要求概要设计数据模型(E-R图) 数据库结构设计主要页面展示项目实现创建项目项目结构展示项目关键 ...
基于Python的Flask框架开发的在线电影网站系统（附源码）
来源丨网络今天给大家分享的是基于Python的Flask框架开发的在线电影网站系统. 项目介绍项目介绍:网站前端采用HTML5编写,使用Bootstrap前端开发框架,后端使用Python3语言编 ...
【目标跟踪】基于matlab GUI帧差法结合卡尔曼滤波行人姿态识别【含Matlab源码 1127期】
⛄一.基于人体特征识别和卡尔曼滤波的行人跟踪算法简介 1 基于体型和行为姿态特征的人体识别算法从红外图像中可以得到目标与背景之间的灰度级差别,从而区分出有生命特征的运动物体,但仅从亮度特征无法区别出 ...
【图像分割】基于matlab GUI医学图像均值聚类+OUST+区域生长法图像分割【含Matlab源码 2210期】
一.图像分割简介 1 图像分割技术图像分割 : 根据灰度 , 颜色 , 纹理等,将图像进行分割. 2 常用的分割技术 2.1 边缘检测法边缘是图像的最重要的特征.边缘是指周围像素灰度有阶跃变化或屋 ...