ROOTQUANT: AUTOMATED ROOT TRAIT QUANTIFICATION FROMMINIRHIZOTRON IMAGES USING DEEP LEARNING

Abstract

A bstract Quantifying root traits such as root length (RL) and root surface area (RSA) from minirhizotron imagery is a valuable approach for overcoming the phenotyping bottleneck that limits understanding and improvement of crop productivity, resource use efficiency and resilience in field experiments. However, current approaches remain labor-intensive, and deep learning (DL) methods suffer from limited generalization ability. We present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision, thereby eliminating the need for pixel-level annotations. The model’s generalization ability was evaluated across species and fine-tuning configurations. The practical applicability of the model was further assessed under field conditions by converting image-derived RL estimates into volumetric root length density (vRLD). Using 118,191 maize and soybean images collected between 2009 and 2020, RootQuant trained on both species achieved an R 2 of 0.90 and an RMSE of 2.9 mm for RL, and an R 2 of 0.88 and an RMSE of 4.2 mm 2 for RSA. The same mixed-species model generalized strongly across species, yielding an 8% relative improvement in R 2 and a 30% lower RMSE on maize compared with the same architecture trained on a single species and applied zero-shot. Image-derived RL predictions converted to vRLD showed the expected depth-dependent decline in vRLD, as was also found by coincident destructive quantification of roots washed out of soil cores. By providing a generalist backbone model trained on a large dataset from two major crop species, RootQuant enables high-throughput simultaneous estimation of two relevant root traits directly from raw imagery without task-specific fine-tuning, thereby accelerating in situ root system analysis and phenotyping applications.

Plant phenotyping relevance

ミニライゾトロン画像から根長・根表面積を推定する深層学習手法を開発し、種間一般化と圃場適用性を評価しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks describe the RootQuant minirhizotron dataset (118,191 maize/soybean images), model architecture, and vRLD validation, but contain no data availability statement, no public repository or deposit identifier, and no authors' URL for images, annotations, code, or trained checkpoints. The only URL-like提及

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