Image analysis-based recognition and quantification of grain number per panicle in rice

Wei Wu; Tao Liu; Ping Zhou; Tianle Yang; Chunyan Li; Xiaochun Zhong; Chengming Sun; Shengping Liu; Wenshan Guo

doi:10.1186/s13007-019-0510-0

Image analysis-based recognition and quantification of grain number per panicle in rice

Plant Methods. 2019 Oct 31:15:122. doi: 10.1186/s13007-019-0510-0. eCollection 2019.

Authors

Wei Wu^{1

2}, Tao Liu^{1

2}, Ping Zhou^{1

2}, Tianle Yang^{1

2}, Chunyan Li^{1

2}, Xiaochun Zhong³, Chengming Sun^{1

2}, Shengping Liu³, Wenshan Guo^{1

2}

Affiliations

¹ 1Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, 225009 China.
² 2Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, 225009 China.
³ 3Key Laboratory of Agro-information Services Technology, Ministry of Agriculture, Beijing, 100081 China.

Abstract

Background: The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains.

Results: In this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both Indica and Japonica subspecies of rice. The linear regression model developed in this study had a grain counting accuracy greater than 96% and 97% for Japonica and Indica rice, respectively. Moreover, while the deep learning model that we used was more time consuming than the linear regression model, the average counting accuracy was greater than 99%.

Conclusions: We developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops.

Keywords: Counting; Grain number per panicle; Image processing; Model; Rice.