L
Lingfeng Duan
Researcher at Huazhong Agricultural University
Publications - 30
Citations - 1349
Lingfeng Duan is an academic researcher from Huazhong Agricultural University. The author has contributed to research in topics: Panicle & Threshing. The author has an hindex of 13, co-authored 30 publications receiving 1024 citations. Previous affiliations of Lingfeng Duan include Huazhong University of Science and Technology.
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Journal ArticleDOI
Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice
Wanneng Yang,Zilong Guo,Chenglong Huang,Lingfeng Duan,Guoxing Chen,Ni Jiang,Wei Fang,Hui Feng,Weibo Xie,Xingming Lian,Gongwei Wang,Qingming Luo,Qifa Zhang,Qian Liu,Lizhong Xiong +14 more
TL;DR: A high-throughput rice phenotyping facility is developed to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period and genome-wide association studies of the 15 traits identify 141 associated loci.
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Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies.
TL;DR: The key plant phenotyping technologies, particularly photonics-based technologies, are discussed, and their current applications in rice (wheat or barley) phenomics are introduced and are confident that these reliable high-throughput phenotypesing tools will give plant scientists new perspectives on the information encoded in the rice genome.
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High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth
Xuehai Zhang,Chenglong Huang,Di Wu,Feng Qiao,Wenqiang Li,Lingfeng Duan,Ke Wang,Yingjie Xiao,Guoxing Chen,Qian Liu,Lizhong Xiong,Wanneng Yang,Jianbing Yan +12 more
TL;DR: Quantitative trait locus (QTL) mapping with a high-density genetic linkage map was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots.
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Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization
Xiong Xiong,Lingfeng Duan,Lingbo Liu,Haifu Tu,Peng Yang,Dan Wu,Guoxing Chen,Lizhong Xiong,Wanneng Yang,Qian Liu +9 more
TL;DR: Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images, and it creates a new opportunity for nondestructive yield estimation.
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A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice
TL;DR: Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday.