一、项目简介本系统是一个基于卷积神经网络CNN的树叶病虫害识别系统能够将树叶图像自动分类为健康、轻度灾害、中度灾害和重度灾害四个等级。系统通过MATLAB GUI提供友好的操作界面用户可选取单张树叶图像进行识别也可查看测试集整体识别准确率同时集成了基本的图像预处理功能供观察分析。系统的核心算法流程如下训练阶段main.m脚本利用imageDatastore从dbx文件夹加载图像根据子文件夹名称自动标注类别标签并按8:2比例随机划分为训练集和测试集。构建的CNN网络包含4个卷积层卷积核尺寸分别为3×3、5×5、5×5、3×3通道数依次为32、16、16、32、4个批归一化层、ReLU激活层和4个最大池化层最后连接全连接层输出4维、Softmax层和分类层。输入图像尺寸为300×400×3。训练采用SGDM优化器最大训练轮数25轮批大小20张梯度阈值1。训练完成后保存模型。识别阶段GUI通过“选取图像”按钮加载待识别图像并显示在原始图像面板点击“识别”按钮调用预训练模型进行分类识别结果如“中度灾害”显示在编辑框中。系统还提供“显示测试集结果”功能批量预测测试集并计算总体准确率同时集成了灰度化和二值化预处理按钮用于展示图像处理中间结果。二、部分源码function pushbutton1_Callback(hObject, eventdata, handles) % 选取图像% hObject handle to pushbutton1 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global I[fn,pn,~]uigetfile(*.jpg,请选择所要识别的图像);I imread([pn fn]);axes(handles.axes1);imshow(I);title(原始图像);% --- Executes on button press in pushbutton2.function pushbutton2_Callback(hObject, eventdata, handles) % 进行识别global Iload(CNNnet.mat);y_pred classify(CNNnet,I);% disp(y_pred);set(handles.edit1,string,y_pred);function edit1_Callback(hObject, eventdata, handles)% --- Executes during object creation, after setting all properties.function edit1_CreateFcn(hObject, eventdata, handles)if ispc isequal(get(hObject,BackgroundColor), get(0,defaultUicontrolBackgroundColor))set(hObject,BackgroundColor,white);end% --- Executes on button press in pushbutton3.function pushbutton3_Callback(hObject, eventdata, handles) % 显示整个测试集的结果% hObject handle to pushbutton3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)%% 加载数据allImages imageDatastore(dbx, ...IncludeSubfolders ,true, ...LabelSource , foldernames );% 图像加载为图像数据存储% imageDatastore函数会根据文件夹名称自动标记图像% 划分训练集(80%)和测试集(20%)[imgsTrain,imgsTest] splitEachLabel(allImages,0.8,randomized);load(CNNnet.mat);y_pred classify(CNNnet,imgsTest); % 使用训练好的网络测试accuracy mean(y_pred imgsTest.Labels);% 计算准确率set(handles.text2,string,[总体准确率: ,num2str(100*accuracy),%],FontSize,12);function edit2_Callback(hObject, eventdata, handles)% --- Executes during object creation, after setting all properties.function edit2_CreateFcn(hObject, eventdata, handles)if ispc isequal(get(hObject,BackgroundColor), get(0,defaultUicontrolBackgroundColor))set(hObject,BackgroundColor,white);end% --- Executes during object creation, after setting all properties.function text1_CreateFcn(hObject, eventdata, handles)% --- Executes during object deletion, before destroying properties.function text5_DeleteFcn(hObject, eventdata, handles)% --- Executes on button press in pushbutton4.function pushbutton4_Callback(hObject, eventdata, handles)% hObject handle to pushbutton4 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global Iglobal Dataif size(I,3) 3Data rgb2gray(I);elseData I;endaxes(handles.axes2)imshow(Data);title(灰度图像);% --- Executes on button press in pushbutton5.function pushbutton5_Callback(hObject, eventdata, handles)% hObject handle to pushbutton5 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global Datathresh graythresh(Data);Itim2bw(Data,thresh);axes(handles.axes3)imshow(It);title(二值图像);三、运行结果四、总结本文设计并实现了一个基于CNN卷积神经网络的树叶病虫害识别系统涵盖了数据加载与划分、CNN网络构建、模型训练与保存、单张图像识别、测试集批量评估以及图像预处理展示的完整流程。系统通过GUI实现了图像选取、识别结果显示、准确率展示和预处理可视化等功能具备良好的交互性和可操作性。实验结果表明系统在自建数据集上能够较为有效地对树叶病虫害程度进行分类。系统的主要不足在于一是网络结构较为简单仅4个卷积层对更细粒度的病虫害类型或复杂背景图像的区分能力有限二是数据集规模偏小模型的泛化能力有待在更大规模、更多样化的数据集上进一步验证三是GUI仅提供识别结果文本缺乏对模型决策依据的可视化解释。后续工作可考虑引入预训练模型如ResNet、MobileNet等进行迁移学习以提升识别精度同时扩充数据集覆盖更多树种和病虫害类型并加入Grad-CAM等可解释性分析方法增强模型的可信度。五、代码获取接matlab程序定制和论文设计方向如下图像处理语音识别图像识别目标检测深度学习神经网络强化学习机器学习通信系统信号处理时频分析小波降噪路径规划优化算法智能算法数据处理数学建模文献复现算法复现模型复现等程序包运行成功零基础的可以远程帮你运行赠送安装包。作为初学者遇见不会的问题是非常正常的事情具体代码仿真可通过主页 私信博主。