文章目录基于YOLOv26的智能农业监测系统1. 系统架构1.1 整体架构1.2 技术栈2. 核心功能模块2.1 数据采集模块基于YOLOv26的智能农业监测系统1. 系统架构1.1 整体架构本系统采用分层架构设计主要包含以下核心模块数据采集模块通过固定摄像头、无人机和传感器获取农业数据数据预处理模块对原始数据进行标准化、增强和地理坐标处理目标检测模块基于YOLOv26检测作物、病虫害、杂草等生长分析模块分析作物的生长状态和健康状况产量预测模块预测作物产量地理信息处理模块处理地理坐标和空间分析决策支持模块根据检测结果生成农业管理建议结果可视化模块将检测和分析结果可视化部署与集成模块实现系统在边缘设备和云端的部署与现有农业管理系统集成1.2 技术栈类别技术/库版本用途核心框架YOLOv26v1.0目标检测数据处理OpenCV4.8.0图像处理数据增强Albumentations1.3.1数据增强模型训练PyTorch2.0.0模型训练地理信息GDAL3.6.0地理坐标处理无人机控制MAVSDK1.40.0无人机控制传感器数据PySerial3.5传感器数据采集设备控制Arduino-Python1.0.0农业设备控制模型部署ONNX1.14.0模型导出可视化matplotlib3.7.1数据可视化2. 核心功能模块2.1 数据采集模块功能描述通过固定摄像头、无人机和传感器获取农业数据支持多源数据融合包括可见光、多光谱和热成像数据实现数据的实时传输和存储代码实现importcv2importnumpyasnpimporttimeimportthreadingfromdatetimeimportdatetimefrommavsdkimportSystemimportasyncioimportserialimportosclassDataCollector:def__init__(self,config):self.configconfig self.cameras{}self.sensors{}self.data_queue[]self.runningFalsedefinitialize_cameras(self):初始化摄像头forcam_id,cam_configinself.config.get(cameras,{}).items():try:capcv2.VideoCapture(cam_config[index])cap.set(cv2.CAP_PROP_FRAME_WIDTH,cam_config.get(width,1280))cap.set(cv2.CAP_PROP_FRAME_HEIGHT,cam_config.get(height,720))self.cameras[cam_id]capprint(fCamera{cam_id}initialized)exceptExceptionase:print(fFailed to initialize camera{cam_id}:{e})definitialize_sensors(self):初始化传感器forsensor_id,sensor_configinself.config.get(sensors,{}).items():try:serserial.Serial(portsensor_config[port],baudratesensor_config.get(baudrate,9600),timeoutsensor_config.get(timeout,1))self.sensors[sensor_id]serprint(fSensor{sensor_id}initialized)exceptExceptionase:print(fFailed to initialize sensor{sensor_id}:{e})defcapture_from_camera(self,cam_id):从指定摄像头捕获图像ifcam_idinself.cameras:capself.cameras[cam_id]ret,framecap.read()ifret:timestampdatetime.now().isoformat()return{frame:frame,timestamp:timestamp,source:fcamera_{cam_id}}returnNonedefcapture_from_all_cameras(self):从所有摄像头捕获图像frames[]forcam_idinself.cameras:frame_dataself.capture_from_camera(cam_id)ifframe_data:frames.append(frame_data)returnframesdefread_from_sensor(self,sensor_id):从指定传感器读取数据ifsensor_idinself.sensors:serself.sensors[sensor_id]try:dataser.readline().decode(utf-8).strip()timestampdatetime.now().isoformat()return{data:data,timestamp:timestamp,source:fsensor_{sensor_id}}exceptExceptionase:print(fFailed to read from sensor{sensor_id}:{e})returnNonedefread_from_all_sensors(self):从所有传感器读取数据sensor_data[]forsensor_idinself.sensors:dataself.read_from_sensor(sensor_id)ifdata:sensor_data.append(data)returnsensor_dataclassUAVDataCollector:def__init__(self,drone_ip127.0.0.1,drone_port50040):self.droneSystem()self.drone_ipdrone_ip self.drone_portdrone_portasyncdefconnect(self):连接到无人机awaitself.drone.connect(system_addressfudp://{self.drone_ip}:{self.drone_port})print(Connected to drone)asyncdefplan_mission(self,field_coords,altitude50):规划巡检任务# 简化实现实际应考虑地形、风向等因素waypoints[]forcoordinfield_coords:waypoints.append((coord[0],coord[1],altitude))returnwaypointsasyncdefexecute_mission(self,waypoints,image_save_dir):执行飞行任务并采集数据ifnotwaypoints:print(No mission planned)return[]# 起飞awaitself.drone.action.arm()awaitself.drone.action.takeoff()awaitasyncio.sleep(5)# 等待无人机稳定collected_images[]# 按路径飞行并采集图像fori,waypointinenumerate(waypoints):print(fFlying to waypoint{i1}:{waypoint})# 飞行到目标点awaitself.drone.action.goto_location(waypoint[0],waypoint[1],waypoint[2],0# 最后一个参数是偏航角)# 等待到达目标点awaitasyncio.sleep(10)# 采集图像模拟timestampdatetime.now().strftime(%Y%m%d_%H%M%S)image_pathf{image_save_dir}/uav_image_{i}_{timestamp}.jpg# 创建模拟图像imagenp.zeros((480,640,3),dtypenp.uint8)cv2.putText(image,fField Image{i},(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)cv2.imwrite(image_path,image)collected_images.append(image_path)# 返航awaitself.drone.action.return_to_launch()awaitasyncio.sleep(10)awaitself.drone.action.disarm()print(fMission completed. Collected{len(collected_images)}images)returncollected_imagesclassDataCollectorManager:def__init__(self,config):self.configconfig self.camera_collectorDataCollector(config)self.uav_collectorUAVDataCollector()self.data_storage[]defstart_collection(self):启动数据采集self.camera_collector.initialize_cameras()self.camera_collector.initialize_sensors()self.runningTrue# 启动摄像头采集线程defcamera_collection_loop():whileself.running:framesself.camera_collector.capture_from_all_cameras()forframe_datainframes:self.data_storage.append(frame_data)time.sleep(1)# 每秒采集一次# 启动传感器采集线程defsensor_collection_loop():whileself.running:sensor_dataself.camera_collector.read_from_all_sensors()fordatainsensor_data:self.data_storage.append(data)time.sleep(5)# 每5秒采集一次self.camera_threadthreading.Thread(targetcamera_collection_loop)self.camera_thread.daemonTrueself.camera_thread.start()self.sensor_threadthreading.Thread(targetsensor_collection_loop)self.sensor_thread.daemonTrueself.sensor_thread.start()print(Data collection started)defstop_collection(self):停止数据采集self.runningFalseifhasattr(self,camera_thread):self.camera_thread.join(timeout2)ifhasattr(self,sensor_thread):self.sensor_thread.join(timeout2)# 释放摄像头资源forcam_id,capinself.camera_collector.cameras.items():try:cap.release()except:pass# 关闭传感器forsensor_id,serinself.camera_collector.sensors.items():try:ser.close()except:passprint(Data collection stopped)asyncdefrun_uav_inspection(self,field_coords,image_save_dir):运行无人机巡检awaitself.uav_collector.connect()waypointsawaitself.uav_collector.plan_mission(field_coords)returnawaitself.uav_collector.execute_mission(waypoints,image_save_dir)# 示例用法config{cameras:{field1:{index:0,width:1280,height:720},field2:{index:1,width:1280,height:720}},sensors:{soil_moisture:{port:COM3,baudrate:9600},temperature:{port:COM4,baudrate:9600}}}managerDataCollectorManager(config)manager.start_collection()# 运行无人机巡检异步asyncdefrun_uav_inspection():field_coords[(39.9042,116.4074),(39.9052,116.4074),(39.9052,116.4084),(39.9042,116.4084)]imagesawaitmanager.run_uav_inspection(field_coords,data/uav_images)returnimages# 运行10秒后停止采集time.sleep(10)manager.stop_collection()print(fCollected{len(manager.data_storage)}data points)