Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework.

Abstract

In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R2 values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments.

Plant phenotyping relevance

画像からイネ苗の生育形質を推定するCNNベースのエンドツーエンド手法を開発・比較評価しており、植物表現型取得が研究の中心である。

Code and data availability

The supplied article blocks describe a rice seedling image dataset (984 images, 504 regression samples) and a hybrid UNet+ResNet50 model, but contain no data availability statement, no public repository deposit, and no authors' URL for code, data, or trained models. No paper-specific public asset is actionable.

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