{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lu X"],"funding":["Fundamental Research Funds for the Central Universities","Key Research and Development Program of Zhejiang Province","the National Key R & D Program"],"pagination":["0265"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11499587"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6"],"pubmed_abstract":["The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at"],"journal":["Plant phenomics (Washington, D.C.)"],"pubmed_title":["Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery."],"pmcid":["PMC11499587"],"funding_grant_id":["2021YFD2000104","226-2022-00217","2020C02002"],"pubmed_authors":["Yang X","Shu Q","Shen Z","Lu X","Xie J","Cen H","Shen Y","Chen S"],"additional_accession":[]},"is_claimable":false,"name":"Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.","description":"The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-18T14:58:39.362Z","creation":"2025-04-07T01:23:31.326Z"},"accession":"S-EPMC11499587","cross_references":{"pubmed":["39449974"],"doi":["10.34133/plantphenomics.0265"]}}