The Invisible Frontline: High-Tech Root Imaging for Crop Stress Adaptation.

Abstract

ABSTRACT Roots are crucial for enhancing crop resilience to abiotic stresses, including drought, salinity, cold, nutrient deficiency, and metal toxicity. Root system architecture and morphological traits play a significant role in enabling plants to access water and nutrients under stress conditions. However, the study of roots is challenging due to their underground nature. Here, we review advancements in high‐throughput root phenotyping methodologies that enable the non‐destructive and large‐scale analysis of root traits in controlled conditions. These include soil‐less two‐dimensional platforms, such as hydroponics and gel‐based systems, and soil‐based systems like Rhizotrons and RhizoTubes. Additionally, cutting‐edge three‐dimensional soil‐less systems and soil‐based imaging technologies, such as x‐ray‐computed tomography and magnetic resonance imaging, have significantly improved the precision of root trait analysis. Computational tools, including machine learning algorithms, are also transforming root phenotyping by automating image segmentation, trait extraction, and data analysis. Case studies and examples described here demonstrate the successful application of these methods in identifying stress‐specific root traits that improve resilience to various abiotic stresses in monocots, dicots, and legumes. Despite these advancements, challenges such as high costs, scalability, and environmental variability persist. Integrating laboratory and field‐based phenotyping systems can address these limitations and lead the way for more effective breeding programs to improve crop resilience against climate change.

Plant phenotyping relevance

根系形質のハイスループット画像化・計測法と計算解析を体系的にレビューしており、植物フェノタイピング手法が中心である。

Code and data availability

This is a review article on high-throughput root phenotyping. The supplied blocks contain no paper-specific phenotype datasets, root images, sensor/3D inputs, author analysis code, trained models, or supplements with such assets. All software tools (RhizoVision Explorer, SmartRoot, etc.) and studies cited are prior/thi

No evidence-backed public reproduction asset is currently recorded.