Soybean Seedling Root Segmentation Using Improved U-Net Network.

Abstract

Soybean seedling root morphology is important to genetic breeding. Root segmentation is a key technique for identifying root morphological characteristics. This paper proposed a semantic segmentation model of soybean seedling root images based on an improved U-Net network to address the problems of the over-segmentation phenomenon, unsmooth root edges and root disconnection, which are easily caused by background interference such as water stains and noise, as well as inconspicuous contrast in soybean seedling images. Soybean seedling root images in the hydroponic environment were collected for annotation and augmentation. A double attention mechanism was introduced in the downsampling process, and an Attention Gate mechanism was added in the skip connection part to enhance the weight of the root region and suppress the interference of background and noise. Then, the model prediction process was visually interpreted using feature maps and class activation mapping maps. The remaining background noise was removed by connected component analysis. The experimental results showed that the Accuracy, Precision, Recall, F1-Score and Intersection over Union of the model were 0.9962, 0.9883, 0.9794, 0.9837 and 0.9683, respectively. The processing time of an individual image was 0.153 s. A segmentation experiment on soybean root images was performed in the soil-culturing environment. The results showed that this proposed model could extract more complete detail information and had strong generalization ability. It can achieve accurate root segmentation in soybean seedlings and provide a theoretical basis and technical support for the quantitative evaluation of the root morphological characteristics in soybean seedlings.

Plant phenotyping relevance

改良U-Netによるダイズ幼苗根画像のセグメンテーション手法を開発・評価し、根形態特性の定量評価を目的としているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper describes a self-built soybean seedling root image dataset (36 scanned images, annotated and augmented) and an improved U-Net model, but no block contains any public deposit, availability statement, or authors' URL for the dataset, images, code, or trained model. The only URLs present are the MDPI DOI and CC-

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