Non-destructive measurement of rice grain size based on panicle structure using deep learning method

Abstract

Abstract Rice grain size, grain length and grain width, are very important traits directly related to rice yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice. Traditional measurement methods still mainly rely on manual labor, which is time-consuming, labor-intensive, and error-prone. In this study, a novel method, dubbed “GSM-Method”, based on convolutional neural network and traditional image processing technology was developed for efficient and precise measurement of rice grain size parameters on rice panicle structure. Firstly, primary branch images of rice panicles were collected at the same height to build image database. Then, the grain detection model using convolutional neural network was established for grain recognition and localization. Subsequently, the calibration value was obtained through traditional image processing technology. Finally, the “GSM-Method” integrated with grain detection model and calibration value was developed for automatic measurement of grain size. The performance of the developed GS-Method was evaluated through testing 60 primary branch images. The test results showed that the root mean square error (RMSE) of grain length for two rice varieties (Huahang15 and Qingyang) were respectively 0.26 mm and 0.30 mm, while the corresponding RMSE of grain width was 0.27mm and 0.31mm, respectively. The proposed algorithm can provide an effective, convenient and low-cost tool for yield evaluation and breeding research.

Plant phenotyping relevance

イネ穎果の長さ・幅という植物形質を画像と深層学習で自動計測する手法を開発し、精度評価まで行っており、フェノタイピング手法が中心である。

Code and data availability

The paper describes a rice panicle image dataset (478 images) and a GSM-Method pipeline, but contains no public deposit, availability statement, or authors' URL for the dataset, annotations, code, or trained model. All allowed URLs correspond to cited third-party tools/references (LabelImg, FPN, GIoU, ImageJ, priorwork

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