Extraction of plant parenchyma by computer image processing technology

Abstract

People are increasingly using different kinds of plant products, such as wood, but there are many kinds of wood and it is difficult to analyze and identify them, so how to use auxiliary equipment to analyze wood and achieve the goal of accurate wood identification without damaging the product itself has become one of the important problems to be solved in the field of wood research. The axial thin-walled tissue has important wood grain information and it is one of the important features for wood identification. In this paper, we studied the microscopic images of broadleaf wood, and obtained the microstructure images of wood cross-section by photographing, and extracted the complete axial thin-walled tissue morphology of wood by using computer image processing technology and other ways about computer vision. Firstly, the axial thin-walled wood images were de-noised to eliminate some noise effects, so as to facilitate the separation of the axial thin-walled wood; then the images were processed by mathematical morphology to successfully extract the axial thin-walled wood and duct morphology from the cross-sectional images of broadleaf wood; finally, the axial thin-walled wood was separated from the duct by calculating the area of the closed area.

Improved plant parenchyma extraction technology using artificial intelligence algorithms

“…The previous paper "Extraction of plant parenchyma by computer image processing technology" mentioned the method of using image processing and other computer visionrelated technologies for extraction of plant parenchyma [1]. This paper analyzes and verifies by experiments that the effect of this method is better.…”

“…Using equation ( 12) from [1], the threshold value of the grayscale image can be calculated, and then the image is transformed into a binary image, and figure 3 and figure 4 show the effect of the binarization process. In figure 5, it can be seen that after the binarization process, the morphology of the axial thin-walled tissue and ducts is more evident, which facilitates the subsequent processing of the images using mathematical morphology and the successful separation of the morphology of the axial thin-walled tissue and ducts.…”