{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16"],"submitter":["Zhao Z"],"pubmed_abstract":["<h4>Introduction</h4>The Leaf Area Index (LAI) is a critical biophysical parameter for assessing crop canopy structure and health. Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors offer a high-throughput solution for LAI estimation, but flight altitude compromises between efficiency and image resolution, ultimately impacting accuracy. This study investigates the integration of super-resolution (SR) image reconstruction with multi-sensor data to enhance LAI estimation for soybeans across varying UAV flight altitudes.<h4>Methods</h4>RGB and multispectral images were captured at four flight altitudes: 15 m, 30 m, 45 m, and 60 m. The acquired images were processed using several SR algorithms (SwinIR, Real-ESRGAN, SRCNN, and EDSR). Texture features were extracted from the RGB"],"journal":["Frontiers in plant science"],"pagination":["1700660"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12675413"],"repository":["biostudies-literature"],"pubmed_title":["UAV multi-source data fusion with super-resolution for accurate soybean leaf area index estimation."],"pmcid":["PMC12675413"],"pubmed_authors":["Jiang Z","Zhang X","Yao H","Zhao Z","Zeng D"],"additional_accession":[]},"is_claimable":false,"name":"UAV multi-source data fusion with super-resolution for accurate soybean leaf area index estimation.","description":"<h4>Introduction</h4>The Leaf Area Index (LAI) is a critical biophysical parameter for assessing crop canopy structure and health. Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors offer a high-throughput solution for LAI estimation, but flight altitude compromises between efficiency and image resolution, ultimately impacting accuracy. This study investigates the integration of super-resolution (SR) image reconstruction with multi-sensor data to enhance LAI estimation for soybeans across varying UAV flight altitudes.<h4>Methods</h4>RGB and multispectral images were captured at four flight altitudes: 15 m, 30 m, 45 m, and 60 m. The acquired images were processed using several SR algorithms (SwinIR, Real-ESRGAN, SRCNN, and EDSR). Texture features were extracted from the RGB","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-05T23:30:02.837Z","creation":"2026-05-23T03:13:39.493Z"},"accession":"S-EPMC12675413","cross_references":{"pubmed":["41358364"],"doi":["10.3389/fpls.2025.1700660"]}}