ArticleFrontiers in plant science2022
Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A dual-branch perception and hybrid attention integrated framework for temporal remote estimation of wheat leaf biomass.Frontiers in plant science · 2026Article
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning.Plant methods · 2025Article
- High-throughput method for improving rice AGB estimation based on UAV multi-source remote sensing image feature fusion and ensemble learning.Frontiers in plant science · 2025Article
- Spatio-temporal mapping of leaf area index in rice: spectral indices and multi-scale texture comparison derived from different sensors.Frontiers in plant science · 2024Article
- Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.Frontiers in plant science · 2024Article
- High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models.Plant phenomics (Washington, D.C.) · 2024Article
- Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.Frontiers in plant science · 2023Article
- SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery.Plant phenomics (Washington, D.C.) · 2023Article
- Phenological stage and vegetation index for predicting corn yield under rainfed environments.Frontiers in plant science · 2023Article
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13 authors.
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Abstract
Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R
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