Artificial intelligence enables the identification and quantification of arbuscular mycorrhizal fungi in plant roots

Abstract

SummarySoil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current approaches to quantifying the extent of root colonisation and the relative abundance of intraradical hyphal structures in mutant roots rely on staining and human scoring involving simple, yet repetitive tasks prone to variations between experimenters.We developed the software AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks.AMFinder delivered high-confidence predictions on image datasets of colonised roots ofMedicago truncatula,Lotus japonicus,Oryza sativaandNicotiana benthamianaobtained via flatbed scanning or digital microscopy enabling reproducible and transparent data analysis. A streamlined protocol for sample preparation and imaging allowed us to quantify dynamic increases in colonisation in whole root systems over time.AMFinder adapts to a wide array of experimental conditions. It enables accurate, reproducible analyses of plant root systems and will support better documentation of AM fungal colonisation analyses.AMFinder can be accessed here:https://github.com/SchornacklabSLCU/amfinder.git