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

Abstract

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 http://taim.imlab.jp/.

Quantifying root colonization by a symbiotic fungus using automated image segmentation and machine learning approaches

“…Even if it is based on objective traits, microscopic quantification is subject to observer bias and performs best when the same person analyses all samples 19 . The present study thus evaluated the reliability of two semi-automated image analysis methods in comparison to manual scoring 6 , 20 demonstrating that image analysis is suitable for ranking samples according to root colonization intensity, in analogy with recent studies 21 , 22 .…”

“…Nevertheless, a commercial software has the advantage of regular and coordinated upgrades by the producer. Recently, two other AM quantifying machine learning based methods were developed, based on (and aimed to replace) a different manual quantification method, the so-called grid line intersection technique 21 , 22 . Indeed, such methods were described to reliably discriminate among fungal structures (arbuscules, hyphae and vesicles).…”

Quantification of arbuscular mycorrhizal fungi root colonization in wheat, tomato, and leek using absolute qPCR

“…The latter method has shown improved agreement with results obtained by micrography and image analysis (Kokkoris et al 2019 ). Furthermore, recent publications have highlighted the potential of artificial intelligence methods particularly deep learning approaches (Evangelisti et al 2021 ) or convolutional neural networks (Muta et al 2022 ) to revolutionize AMF quantification. These advanced techniques offer several advantages, including a reduction in time and decreased dependence on subjective observer interpretations.…”