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An integrated rice panicle phenotyping method based on X-ray and rgb scanning and deep learning

The Crop Journal 2021年第1期9卷 42-56页

作者： Lejun Yu Jiawei Shi Chenglong Huang Lingfeng Duan Di Wu Debao Fu Changyin Wu Lizhong Xiong Wanneng Yang Qian Liu Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for OptoelectronicsKey Laboratory of Ministry of Education for Biomedical PhotonicsDepartment of Biomedical EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene ResearchAgricultural Bioinformatics Key Laboratory of Hubei ProvinceCollege of EngineeringHuazhong Agricultural UniversityWuhan 430070HubeiChina School of Biomedical Engineering Hainan UniversityHaikou 570228HainanChina

Rice panicle phenotyping is required in rice breeding for high yield and grain *** fully evaluate spikelet and kernel traits without threshing and hulling,using X-ray and rgb scanning,we developed an integrated rice panicle phenotyping system and a corresponding image analysis *** compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy(R~2 of 0.99)and *** R-CNN was also applied to indica and japonica classification and achieved 91%*** proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding.

关键词： Rice(O.satiua) Panicle traits rgb imaging X-ray scanning Faster R-CNN

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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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The Crop Journal 2022年第5期10卷 1303-1311页

作者： 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 CentreChinese Academy of Agricultural Sciences(CAAS)Beijing 100081China Electronic Information School Foshan PolytechnicFoshan 528137GuangdongChina International Maize and Wheat Improvement Centre(CIMMYT)China Office c/o CAASBeijing 100081China Department of Plant Sciences Quaid-i-Azam UniversityIslamabad 44000Pakistan

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 *** 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 ***-4 DS, ***-7 DS, and ***-7 DL were identified based on MSN, ISN and VSN data, while ***-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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