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

查看全文 作　　者：Lei [1]Li;Muhammad Adeel [1]Hassan;Shurong [1]Yang;Furong [2]Jing;Mengjiao [1]Yang;Awais [1,3,4]Rasheed;Jiankang [1]Wang;Xianchun [1]Xia;Zhonghu [1,3]He;Yonggui [1]Xiao

机构地区：[1]Institute of Crop Sciences,National Wheat Improvement Centre,Chinese Academy of Agricultural Sciences(CAAS),Beijing 100081,China;[2]Electronic Information School,Foshan Polytechnic,Foshan 528137,Guangdong,China;[3]International Maize and Wheat Improvement Centre(CIMMYT)China Office,c/o CAAS,Beijing 100081,China;[4]Department of Plant Sciences,Quaid-i-Azam University,Islamabad 44000,Pakistan

出　　处：《The Crop Journal》索引2022年第10卷第5期,共9页

基　　金：funded by the National Natural Science Foundation of China (31671691, 3171101265, and 31961143007);the National Key Research and Development Program of China(2016YFD0101804);the Fundamental Research Funds for the Institute Planning in Chinese Academy of Agricultural Sciences(S2018QY02)。 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 selection 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 660 K 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(R^(2)) between ISN and VSN was 0.83, which was higher than that between ISN and MSN(R^(2)= 0.51), and between VSN and MSN(R^(2)= 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-4 DS, Qsnyz.caas-7 DS, and QSnyz.caas-7 DL were identified based on MSN, ISN and VSN data, while QSnyz.caas-7 DS 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. 关键词：Deeping learning High-throughput phenotyping QTL mapping RGB imaging

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

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