Size measurement and filled/unfilled detection of rice grains using backlight image processing

Frontiers in Plant Science

Plant Science

Yunting Lan¹  Hao Gong¹  Yuhao Zhou¹  Zhiqi Wang¹  Long Qi²  Xiao Feng²  Zhiwei Zeng³  Wei Zou⁴ 

[1] College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China;College of Engineering, South China Agricultural University, Guangzhou, Guangdong, China;Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China;Department of Agricultural Engineering Technology, University of Wisconsin-River Falls, River Falls, WI, United States;R&D Center, Top-Leading Intelligent Technology Co. ltd., Guangzhou, Guangdong, China;

关键词: rice;    breeding;    physical traits;    computer vision;    image processing;   

DOI  :  10.3389/fpls.2023.1213486

 received in 2023-04-28, accepted in 2023-09-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.

【 授权许可】

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Copyright © 2023 Feng, Wang, Zeng, Zhou, Lan, Zou, Gong and Qi