Arbuscular mycorrhizas are the most widespread plant symbiosis and involve the majority of crop plants. This interaction between plant roots and Glomeromycetes grants the green host a preferential access to soil mineral nutrients and water, supporting plant health, biomass production and resistance to both abiotic and biotic stresses. The nutritional exchanges at the core of this symbiosis take place inside the living cortical root cells, which are diffusely colonized by specialized fungal structures called arbuscules. For this reason, the vast majority of studies investigating arbuscular mycorrhizas and their applications in agriculture require a precise quantification of root colonization intensity. To this aim, several manual methods have been used for decades to estimate the extension of intraradical fungal structures, mostly based on optical microscopy observations and individual assessment of fungal abundance in the root tissues. Such methods are extremely time consuming, based on the ability of trained operators and subject to errors. We propose a novel semi-automated approach to quantify AM colonization based on digital image analysis, comparing two methods based on image thresholding and machine learning to manual classification. Our results indicate in machine learning a very promising tool for accelerating, simplifying and standardizing this critical type of analysis, with a direct potential interest for applicative and basic research.
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