High-frequency in situ root phenotyping achieved by automated Minirhizotron image acquisition and analysis

Abstract

Minirhizotron technique (MR), an image-based root phenotyping technology, has expanded our understanding of in situ root responses to changing environmental conditions. However, the conventional manual approaches to capturing and analyzing images are time-consuming, thus constraining the size and frequency of sampling and interpretation. To address this bottleneck, an automated MR system was developed, which worked in a fully automatic manner according to the pre-set schedule. Users can remotely control the system and download images. This system was tested in a net house by imaging bell pepper roots on a daily basis, which shows superior performance over commercial manual MR systems in terms of image resolution and sampling frequency. Besides, an image analysis model was built on convolutional neural networks to estimate root length from MR images directly without segmentation in the training process. This model was trained on a dataset of ~18,000 tomato root images taken by a manual MR camera and was used for estimating root length on 832 bell pepper root images taken by the automated MR camera. The high correlation coefficient (R 2 = 0.854) between the model estimation and manual measurement proved that this model generalizes well over different crop roots and camera types. Therefore, high-frequency in situ root phenotyping can be achieved by the automated Minirhizotron image acquisition and analysis tools proposed here.

Plant phenotyping relevance

自動ミニリゾトロンによる根画像取得とCNNによる根長推定を開発し、手動測定との性能検証まで行っており、植物表現型取得手法が研究の中心である。

Code and data availability

The supplied blocks (title page and NAPPN abstract) describe an automated Minirhizotron system and a CNN root-length model trained on ~18,000 tomato root images, but contain no data availability statement, repository deposit, or public URL for the images, trained model, or analysis code. No paper-specific public asset,

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