Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies

文献类型： 外文期刊

第一作者： Li, Lei

作者： Li, Lei;Yang, Shurong;Yang, Mengjiao;Rasheed, Awais;Wang, Jiankang;Xia, Xianchun;He, Zhonghu;Xiao, Yonggui;Jing, Furong;He, Zhonghu;Rasheed, Awais

作者机构：

关键词： Deeping learning; High-throughput phenotyping; QTL mapping; RGB imaging

期刊名称：CROP JOURNAL （ 影响因子：4.647； 五年影响因子：5.781 ）

ISSN： 2095-5421

年卷期： 2022 年 10 卷 5 期

页码：

收录情况： SCI

摘要： Spike number (SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selec-tion and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks (Faster R-CNN) on Red-Green-Blue (RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660K SNP array. Analysis of manual spike number (MSN) in the field, image-based spike number (ISN), and verification of spike number (VSN) by Faster R-CNN revealed significant variation (P < 0.001) among genotypes, with high heritability ranged from 0.71 to 0.96. The coefficients of determination (R2) between ISN and VSN was 0.83, which was higher than that between ISN and MSN (R2 = 0.51), and between VSN and MSN (R2 = 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL Qsnyz.caas-4DS, Qsnyz.caas-7DS, and QSnyz.caas-7DL were identified based on MSN, ISN and VSN data, while QSnyz.caas-7DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat. (c) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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