Quantifying Root Colonization in Arbuscular Mycorrhizas by Image Segmentation and Machine Learning

Abstract

MotivationArbuscular mycorrhizas are the most widespread plant symbioses and involve the majority of crop plants. The beneficial interaction between plant roots and a group of soil fungi (Glomeromycotina) 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 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 the intensity of root colonization. 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. ResultsHere we propose a novel semi-automated approach to quantify AM colonization based on digital image analysis and compare two methods based on image thresholding and machine learning. 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.Contactivan.sciascia@unito.it; andrea.genre@unito.it

Mycorrhization in trees: ecology, physiology, emerging technologies and beyond

“…Machine learning using the Zeiss Zen Intellesis application demonstrated more accurate and efficient estimation of AM colonized structures compared to conventional methods (Sciascia et al . 2021). Expression profiles of Medicago roots in response to mycorrhization were obtained from four separate studies and combined using a meta‐analysis approach.…”