Improved plant parenchyma extraction technology using artificial intelligence algorithms
Автор: Chen Jike, Zhao Qian
Журнал: Современные инновации, системы и технологии.
Рубрика: Управление, вычислительная техника и информатика
Статья в выпуске: 2(4), 2022 года.
The previous studies have described the extraction of plant parenchyma by computer image processing technology, and the purpose of this paper is to verify the effectiveness of the algorithm., this paper implements the algorithm by using Matlab language, and designs several groups of experiments. The experimental results show that: when denoising, using 9*9 as a template to perform median filtering on the image has a better effect, and block binarization facilitates the extraction of axial parenchyma; when processing mathematical morphology, using 3*3 Axial parenchyma and vessel morphology can be successfully extracted from cross-sectional images of broad-leaved wood after dilation of the image by cross-shaped structuring elements and erosion of images by disc-shaped structuring elements with radii ranging from 1 to 10 When calculating the area threshold of the closed area, the area threshold is determined by using 8 domains to mark the area of the closed area and using the area histogram, so that the axial parenchyma can be better separated from the catheter. At present, the method has been experimented in 10 different tree species, all of which have achieved good results. This also fully proves the effectiveness of the artificial intelligence algorithm. The implementation of the algorithm also lays the foundation for future research on intelligent wood recognition based on axial thin-walled tissue morphology; it provides a shortcut to measure the content of axial thin-walled tissue in different tree species; and it is a prelude to the development of an image-based wood recognition system for axial thin-walled tissue.
Artificial intelligence, Computer vision, Mathematical morphology, Parenchyma, Plant, Wood recognition, Extraction
Короткий адрес: https://sciup.org/14124859
IDR: 14124859 | DOI: 10.47813/2782-2818-2022-2-4-0233-0263