A root system architecture measurement pipeline for apple rootstocks

Abstract A robust root system architecture (RSA) is vital for tree health and fruit production of perennial fruit crops across diverse environments over many years. Consequently, RSA holds significant importance for fruit crop breeding programs. However, phenotyping tree roots presents a significant challenge due to their size, complexity, and lack of an existing efficient root phenotyping system. This study aimed to address this gap by developing and validating a root phenotyping pipeline for large, complex, apple root systems. Root traits of both greenhouse and field‐grown apple trees were captured using either a camera or by imaging shaved roots with a flatbed scanner. These images were then analyzed with five root phenotyping software, RhizoVision, ImageJ, DIRT, RootGraph, and PlantCV, as well as visually ranked. Afterward, all these measurements were compared to hand‐measured root traits to evaluate their accuracy. RhizoVision accurately ( p < 0.05) measured all traits for both greenhouse and field‐grown apple root systems. RhizoVision was able to accurately measure surface area and volume of intact crowns in as little as three photos. RootGraph and PlantCV showed varying degrees of accuracy. RhizoVision emerged as the most efficient and accurate method, providing root trait data without requiring extensive preprocessing of images, thus offering a reliable approach for phenotyping large apple root systems.