TAIM: Tool for Analyzing Root Images to Calculate the Infection Rate of Arbuscular Mycorrhizal Fungi

被引：3

作者：

Muta, Kaoru ^{[1
]}

Takata, Shiho ^{[2
]}

Utsumi, Yuzuko ^{[1
]}

Matsumura, Atsushi ^{[2
]}

Iwamura, Masakazu ^{[1
]}

Kise, Koichi ^{[1
]}

机构：

[1] Osaka Prefecture Univ, Grad Sch Engn, Osaka, Japan

[2] Osaka Prefecture Univ, Grad Sch Life & Environm Sci, Osaka, Japan

来源：

FRONTIERS IN PLANT SCIENCE | 2022年 / 13卷

关键词：

arbuscular mycorrhizal fungi; magnified intersections method; computer vision; pattern recognition; deep convolutional neural networks; system development; MICROSCOPY; TRACKING; CLASSIFICATION; SEGMENTATION; COLONIZATION; DIVERSITY; GRAINS; PLANT;

D O I：

10.3389/fpls.2022.881382

中图分类号：

Q94 [植物学];

学科分类号：

071001 ;

摘要：

Arbuscular mycorrhizal fungi (AMF) infect plant roots and are hypothesized to improve plant growth. Recently, AMF is now available for axenic culture. Therefore, AMF is expected to be used as a microbial fertilizer. To evaluate the usefulness of AMF as a microbial fertilizer, we need to investigate the relationship between the degree of root colonization of AMF and plant growth. The method popularly used for calculation of the degree of root colonization, termed the magnified intersections method, is performed manually and is too labor-intensive to enable an extensive survey to be undertaken. Therefore, we automated the magnified intersections method by developing an application named "Tool for Analyzing root images to calculate the Infection rate of arbuscular Mycorrhizal fungi: TAIM. " TAIM is a web-based application that calculates the degree of AMF colonization from images using automated computer vision and pattern recognition techniques. Experimental results showed that TAIM correctly detected sampling areas for calculation of the degree of infection and classified the sampling areas with 87.4% accuracy. TAIM is publicly accessible at .

引用

页数：11

共 52 条

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