基于嵌入式系统的小麦条锈病远程监测平台设计与试验

为了实现小麦条锈病的远程实时监测,设计并搭建了基于嵌入式系统的小麦条锈病远程监测平台,实现了用户对大田小麦条锈病发病状况的实时监测。首先基于Arduino微控制器和42步进电机控制的六棱柱转轴和传送装置结合,通过蓝牙控制六棱柱转轴上的电磁吸附装置吸附金属加工后的载玻片设计了孢子捕捉器,实现了空气中小麦条锈病孢子图像的采集;其次,通过高倍光学显微镜和电子目镜将采集到的孢子图像通过Linux核心板上传至云端服务器,并通过基于Python的图像处理算法对图像进行中值滤波、边缘提取、角点检测等处理实现孢子计数;最后通过基于Android平台的应用软件实现远程查看孢子图像和计数处理结果。试验结果表明,该平台服务器图像处理算法可实现孢子的准确计数,对测试图像的计数准确率为100%,孢子捕捉器的玻片切换成功率为95%。该研究可为大田小麦条锈病的实时监测奠定基础,也可为大田内其他气传病害的监测提供借鉴。

Design and test of wheat stripe rust remote monitoring platform based on embedded system

Wheat stripe rust is an important biological disaster that affects the safe production of wheat in China for a long time. The number of spores of wheat stripe rust is a direct factor affecting its pathogenesis and transmission. At present, it mainly relies on the field sampling and investigation of agricultural technicians to predict and forecast. It is time-consuming and laborious, and difficult to achieve long-term monitoring of diseases, thus affecting the accuracy of forecasting and the timeliness of prevention and control. The existing automatic spore monitoring device also has the problems that the collecting device is mostly in the form of manual replacement of slides, and the direct acquisition of components in the air by a limited area of the slide may result in inaccurate sample collection and too small sample size. In order to further improve the monitoring and forecasting ability of wheat stripe rust, a wheat stripe rust monitoring device was designed and implemented, which based on the internet to build a wheat stripe rust monitoring platform, and based on the embedded system to establish a complete set of wheat stripe rust spore collection and image transmission processing device. Spore acquisition was performed using a slide adsorption device of "Six prism column + Electromagnet + Microscope". Control the up and down movement of the electromagnet to control the up and down movement of the slide; update the slide by controlling the rotation of the hexagonal shaft; obtain the image by controlling the time synchronization of the microscope and the shaft; control the cleaning solvent the smear and the movement of the cleaning block enable the slide to be cleaned. At the same time, a spore counting program based on the server platform was designed to process and analyze the collected slide images. The spore counting program used in this design is based on Python 3.6 and combined with the Skimage image processing package for spore image analysis and processing. The geometry factor feature based method was used, and the number of spores in the microscope field was finally obtained based on the regional attribute values. The experimental results show that the platform server image processing algorithm can achieve accurate counting of spores, the accuracy of counting the test images is 100%; the success rate of the slide switching system is 95%.This study can lay a foundation for the real-time monitoring of wheat stripe rust in the field, and can also provide references for the monitoring of other airborne diseases in the field.