| Biomorphology is the science of studying the shape and structure of animal,plant and microbial components.An important basis for biomorphological research is the rapid and efficient extraction of morphological information from biological growth states and quantitative analysis as required.Due to the slow growth of organism and the long time span,this analysis process is difficult and long-term through traditional manual measurement methods with low efficiency and lots of errors.In tradition microscopy,the tester directly observes under the microscope through the eye.This method is inefficient,and the detection accuracy decreases with the increase of working time.It is necessary to repeatedly test the sample and consume a lot of manpower and material resources.It can be seen that traditional microscopy methods have been difficult to apply to biomorphological testing.At present,the computer hardware and software are developing at a high speed,the computer hardware is updated rapidly,and the hardware operation speed is greatly improved.Through the high-speed operation of the GPU,hardware guarantee is provided for the computer to process images at high speed.At the same time,image processing technology has matured over the years.The research gain of image neural network based on deep learning,such as RCNN,yolo,SSD and other neural networks,greatly improve the accuracy and reliability of image recognition,so that computer vision can be applied to the field of automated production and life,which makes the computer vision of biomorphology possible to analyze and identify quickly and accurately.The research work in this paper mainly uses computer vision to measure and analyze biological morphology,photograph plants and microbe through microscope,combine traditional image method with deep learning,identify and measure microscopic images,and implement the algorithm through vc++.The research work is mainly divided into two parts,the roots of the plant seedlings placed on the medium were continuously photographed at the same time interval,and the growth information of each component of the roots was extracted and the growth state curve was drawn by the traditional image processing method as a quantitative index of root growth;and combining root growth characteristics,photographing plant roots using layered photography.The image processing method combined with traditional image processing and deep learning.The images taken by microbial scanning are extracted,segmented and enhanced by traditional methods and then transmitted to the yolo neural network to identify and count.Chart and report generates after analyzing.The paper first introduces the background of the subject in the introduction,and then introduces the traditional methods used in the processing system such as image smoothing,threshold segmentation,dilation and erosion,edge detection,refinement algorithms and deep learning yolo network.Then introduce the specific design of the software and parallelization methods used in the system.Finally,the research work is summarized and the future prospects are proposed.This paper provides a new method for determination of root shape and microbial morphology of plants,and provide a rapid detection method for subsequent biomorphological research and industrial production,which has high application value in accuracy and speed.It can provide reference for subsequent research in this area. |
