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Root system architecture is critical for plant growth, which is influenced by several edaphic, environmental, genetic, and biotic factors including beneficial and pathogenic microbes. Studying root system architecture and the dynamic changes that occur during a plant’s lifespan, especially for perennial crops growing over multiple growing seasons, is still a challenge because of the nature of their growing environment. We describe the utility of an imaging platform called RhizoVision Crown to study root system architecture of alfalfa, a perennial forage crop threatened by Phymatotrichopsis root rot (PRR) disease. Phymatotrichopsis omnivora is the causal agent of PRR disease that reduces alfalfa stand longevity. During the lifetime of the stand, PRR disease rings enlarge and the field can be categorized into three zones based upon plant status: asymptomatic, disease front and survivor. To study root system architectural changes associated with PRR, a 4-year-old 25.6-ha alfalfa stand infested with PRR was selected at the Red River Farm, Burneyville, OK during October 2017. Line transect sampling was conducted from four actively growing PRR disease rings. At each disease ring, six line transects were positioned spanning 15 m on either side of the disease front with one alfalfa root crown sampled at every 3 m interval. Each alfalfa root crown was imaged with the RhizoVision Crown platform using a backlight and a high-resolution monochrome CMOS camera enabling preservation of the natural root system integrity. The platform’s image analysis software, RhizoVision Analyzer, automatically segmented images, skeletonized, and extracted a suite of features. Data indicated that the survivor plants compensated for damage or loss to the taproot through the development of more lateral and crown roots, and that a suite of multivariate features could be used to automatically classify roots as from survivor or asymptomatic zones. Root growth is a dynamic process adapting to ever changing interactions among various phytobiome components. By utilizing the low-cost, efficient, and high-throughput RhizoVision Crown platform, we quantified these changes in a mature perennial forage crop. |
