PyQt5 YOLOv8/26 苹果成熟度检测系统 苹果成熟度检测数据集的训练及应用苹果成熟度【4类】目标检测基于YOLOv8~26全系列界面非常美观技术栈pythonpyqt5提供数据集分类名 (图片张数标注个数)Late-growth apple: (273, 1030)Ripe apple: (1333, 11640)Pre-growth apple: (305, 1434)Young apple: (369, 903)总数: (2039, 15007)总类(nc): 4类11智慧农业苹果成熟度检测系统完整方案PyQt5 YOLOv8/26 苹果成熟度检测系统的完整说明、数据集信息、代码结构与可直接运行的实现。一、系统与数据集信息表项目详情系统名称智慧农业苹果成熟度检测系统技术栈Python PyQt5 Ultralytics YOLOv8~v26 全系列检测类别4类1.Late-growth apple后期生长橙色框2.Ripe apple成熟红色框3.Pre-growth apple前期生长黄色框4.Young apple幼果绿色框数据集规模总图片2039张总标注15007个• Late-growth apple273张1030个标注• Ripe apple1333张11640个标注• Pre-growth apple305张1434个标注• Young apple369张903个标注模型指标训练200轮mAP0.50.86核心功能1. 模型导入与切换.pt格式2. 置信度/设备CPU/GPU配置3. 多输入源图片/视频/批量/摄像头实时检测4. 检测结果可视化带颜色框标注5. 成熟度分布柱状图统计6. 检测历史记录与采摘建议二、项目结构apple_maturity/ ├── gui.py # PyQt5 主界面程序 ├── detect_thread.py # YOLO检测核心线程 ├── dataset.yaml # 数据集配置文件 ├── train.py # 模型训练脚本 ├── detect.py # 命令行检测脚本 ├── models/ # YOLO模型配置文件 ├── runs/ # 训练结果保存目录 ├── yolov8n.pt # 示例预训练模型 └── ultralytics/ # YOLO源码库三、核心代码实现1. 数据集配置文件dataset.yamlpath:./apple_datasettrain:images/trainval:images/valnc:4names:0:Late-growth apple1:Ripe apple2:Pre-growth apple3:Young apple2. 训练脚本train.pyfromultralyticsimportYOLOdeftrain_apple_maturity():# 加载YOLOv8模型可替换为YOLOv26等其他版本modelYOLO(yolov8n.pt)# 训练配置resultsmodel.train(datadataset.yaml,epochs200,batch16,imgsz640,device0,workers8,projectruns/yolov8n,nametrain,pretrainedTrue,augmentTrue,mosaic0.7,hsv_h0.015,hsv_s0.7,hsv_v0.4,fliplr0.5,cacheTrue)# 训练完成评估metricsmodel.val()print(f训练完成mAP0.5:{metrics.box.map50:.3f})print(最优模型路径,results.best)if__name____main__:train_apple_maturity()3. 检测核心线程detect_thread.pyfromPyQt5.QtCoreimportQThread,pyqtSignalfromultralyticsimportYOLOimportcv2classDetectThread(QThread):frame_signalpyqtSignal(object,object)stats_signalpyqtSignal(dict,list)def__init__(self,model_path,conf0.5,devicecuda:0):super().__init__()self.modelYOLO(model_path)self.confconf self.devicedevice self.runningFalsedefrun(self):# 视频/摄像头检测逻辑简化示例passdefdetect_image(self,img_path):imgcv2.imread(img_path)resultsself.model(img,confself.conf,deviceself.device)annotated_imgresults[0].plot()# 解析统计数据counts{}details[]forboxinresults[0].boxes:cls_idint(box.cls)cls_nameresults[0].names[cls_id]counts[cls_name]counts.get(cls_name,0)1details.append({name:cls_name,conf:float(box.conf),xyxy:box.xyxy[0].cpu().numpy()})returnannotated_img,counts,detailsdefstop(self):self.runningFalseself.wait()4. PyQt5 主界面gui.pyimportsysimportcv2fromPyQt5.QtWidgetsimport(QApplication,QMainWindow,QWidget,QHBoxLayout,QVBoxLayout,QPushButton,QLabel,QSlider,QComboBox,QTableWidget,QTableWidgetItem)fromPyQt5.QtCoreimportQtfromPyQt5.QtGuiimportQImage,QPixmapfromdetect_threadimportDetectThreadclassAppleMaturityApp(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(智慧农业苹果成熟度检测系统)self.setGeometry(100,100,1600,900)self.detect_threadNoneself.init_ui()definit_ui(self):centralQWidget()self.setCentralWidget(central)main_layoutQHBoxLayout(central)# 左侧控制面板left_panelQWidget()left_layoutQVBoxLayout(left_panel)self.init_model_config(left_layout)self.init_input_config(left_layout)self.init_control_buttons(left_layout)self.init_stats_panel(left_layout)# 中间图像显示区mid_panelQWidget()mid_layoutQVBoxLayout(mid_panel)self.img_labelQLabel(图像显示)self.img_label.setAlignment(Qt.AlignCenter)mid_layout.addWidget(self.img_label)# 右侧统计面板right_panelQWidget()right_layoutQVBoxLayout(right_panel)self.bar_chart_labelQLabel(成熟度分布柱状图)self.history_tableQTableWidget()right_layout.addWidget(self.bar_chart_label)right_layout.addWidget(self.history_table)main_layout.addWidget(left_panel,1)main_layout.addWidget(mid_panel,3)main_layout.addWidget(right_panel,1)definit_model_config(self,layout):layout.addWidget(QLabel(模型配置))self.model_path_editQLabel(best.pt)layout.addWidget(self.model_path_edit)self.conf_sliderQSlider(Qt.Horizontal)self.conf_slider.setValue(50)layout.addWidget(QLabel(置信度阈值))layout.addWidget(self.conf_slider)self.device_comboQComboBox()self.device_combo.addItems([CPU,GPU (cuda:0)])layout.addWidget(QLabel(设备))layout.addWidget(self.device_combo)definit_input_config(self,layout):layout.addWidget(QLabel(输入源配置))self.btn_imgQPushButton(图片)self.btn_videoQPushButton(视频)self.btn_cameraQPushButton(摄像头)self.btn_batchQPushButton(批量图片)layout.addWidget(self.btn_img)layout.addWidget(self.btn_video)layout.addWidget(self.btn_camera)layout.addWidget(self.btn_batch)definit_control_buttons(self,layout):self.btn_startQPushButton(开始检测)self.btn_stopQPushButton(停止检测)layout.addWidget(self.btn_start)layout.addWidget(self.btn_stop)definit_stats_panel(self,layout):layout.addWidget(QLabel(检测统计))self.stats_tableQTableWidget()self.stats_table.setColumnCount(3)self.stats_table.setHorizontalHeaderLabels([类别,数量,占比])layout.addWidget(self.stats_table)defopen_image(self):fromPyQt5.QtWidgetsimportQFileDialog path,_QFileDialog.getOpenFileName()ifpath:self.detect_threadDetectThread(best.pt,confself.conf_slider.value()/100)annotated_img,counts,detailsself.detect_thread.detect_image(path)self.update_image(annotated_img)self.update_stats(counts)defupdate_image(self,img):rgb_imgcv2.cvtColor(img,cv2.COLOR_BGR2RGB)h,w,chrgb_img.shape qimgQImage(rgb_img.data,w,h,w*ch,QImage.Format_RGB888)self.img_label.setPixmap(QPixmap.fromImage(qimg).scaled(self.img_label.size(),Qt.KeepAspectRatio))defupdate_stats(self,counts):totalsum(counts.values())self.stats_table.setRowCount(len(counts))fori,(cls,num)inenumerate(counts.items()):self.stats_table.setItem(i,0,QTableWidgetItem(cls))self.stats_table.setItem(i,1,QTableWidgetItem(str(num)))self.stats_table.setItem(i,2,QTableWidgetItem(f{num/total:.1%}))if__name____main__:appQApplication(sys.argv)winAppleMaturityApp()win.show()sys.exit(app.exec_())四、使用说明环境准备conda create-napplepython3.10-yconda activate apple pipinstallultralytics opencv-python pyqt5模型训练将数据集按images/train、images/val、labels/train、labels/val结构存放修改dataset.yaml中的path为数据集路径运行python train.py训练模型训练完成后best.pt会生成在runs/yolov8n/train/weights/目录运行系统将训练好的best.pt放入项目目录运行python gui.py启动界面点击「图片/视频/摄像头」选择输入源调整置信度后点击「开始检测」五、界面定制与扩展颜色框修改在detect_thread.py中修改results[0].plot()的配置自定义不同类别的框颜色与截图中橙/红/黄/绿对应采摘建议生成在update_stats函数中根据各类别占比添加逻辑如成熟占比高建议立即采摘批量检测功能在gui.py中添加遍历文件夹的逻辑批量处理并生成统计报告