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.

Lei Li;Muhammad Adeel Hassan;Shurong Yang;Furong Jing;Mengjiao Yang;Awais Rasheed;Jiankang Wang;Xianchun Xia;Zhonghu He;Yonggui Xiao

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

作物学报（英文版）

[1]Lei Li;Muhammad Adeel Hassan;Shurong Yang;Furong Jing;Mengjiao Yang;Awais Rasheed;Jiankang Wang;Xianchun Xia;Zhonghu He;Yonggui Xiao-.Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies)[J].作物学报（英文版）,2022(05):1303-1311

Zhongmai,Qsnyz,caas,4DS,7DS,QSnyz,7DL

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