ArticlePlant phenomics (Washington, D.C.)2021
The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines.
Article in Plant phenomics (Washington, D.C.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Advances in genomics-driven genetic decoding and genomic design breeding in tomato.Horticulture research · 2026Article
- Hyperspectral phenotyping and GWAS identify novel QTLs for soybean photosynthetic rate.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Leveraging UAV hyperspectral imaging for crop physiology and biochemistry: A comprehensive review of feature extraction and selection methods.Plant phenomics (Washington, D.C.) · 2026Review
- High-throughput estimation of sugarcane phenotypic traits using UAV multispectral data under high-density planting conditions.Plant phenomics (Washington, D.C.) · 2026Article
- Root system architecture and drought adaptation: emerging tools and genetic insights.Frontiers in plant science · 2026Review
- Dynamic maize true leaf area index retrieval with KGCNN and TL and integrated 3D radiative transfer modeling for crop phenotyping.Plant phenomics (Washington, D.C.) · 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
- Detection of the stem-boring damage by pine shoot beetle (Frontiers in plant science · 2025Article
- DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments.Plant methods · 2024Article
- Monitoring of parasite Orobanche cumana using Vis-NIR hyperspectral imaging combining with physio-biochemical parameters on host crop Helianthus annuus.Plant cell reports · 2024Article
- Research on Segmentation Method of Maize Seedling Plant Instances Based on UAV Multispectral Remote Sensing Images.Plants (Basel, Switzerland) · 2024Article
- A deep learning model for rapid classification of tea coal disease.Plant methods · 2023Article
- Testing a Method Based on an Improved UNet and Skeleton Thinning Algorithm to Obtain Branch Phenotypes of Tall and Valuable Trees UsingPlants (Basel, Switzerland) · 2023Article
- Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.Plant phenomics (Washington, D.C.) · 2023Article
- Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Estimation of amino acid contents in maize leaves based on hyperspectral imaging.Frontiers in plant science · 2022Article
- Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum.Plant phenomics (Washington, D.C.) · 2022Article
- Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits.Plant phenomics (Washington, D.C.) · 2022Article
- Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.Frontiers in plant science · 2022Article
Corrections and comments
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11 authors.
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Abstract
Crop traits such as aboveground biomass (AGB), total leaf area (TLA), leaf chlorophyll content (LCC), and thousand kernel weight (TWK) are important indices in maize breeding. How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding. Compared with digital and multispectral images, the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle (UAV) are expected to accurately estimate the similar traits among breeding materials. This study is aimed at exploring the feasibility of estimating AGB, TLA, SPAD value, and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to screen sensitive bands for the maize traits. Partial least squares (PLS) and random forest (RF) algorithms were used to estimate the maize traits. The results can be summarized as follows: The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions. The sensitive bands screened by CARS were more abundant than those screened by SPA. For AGB, TLA, and SPAD value, the optimal combination was the CARS-PLS method. Regarding the TWK, the optimal combination was the CARS-RF method. Compared with the model built by RF, the model built by PLS was more stable. This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.
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