Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning.

Abstract

The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.

Plant phenotyping relevance

画像セグメンテーションと深層学習によりイネ籾の充実・不稔を識別し、種子登熟率という植物形質を自動・高スループット推定する手法が研究の中心である。

Code and data availability

The paper's rice panicle image dataset used for seed setting rate phenotyping is publicly deposited on Kaggle per the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited; supplementary material contains only figures/tables, not datasets or code.