ArticlePlant phenomics (Washington, D.C.)2024
UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Combining RGB imaging with a two-stage deep learning method to reveal genetic variation of wheat sprouting traits.Plant phenomics (Washington, D.C.) · 2026Article
- A dual-branch perception and hybrid attention integrated framework for temporal remote estimation of wheat leaf biomass.Frontiers in plant science · 2026Article
- Field phenotyping for soybean density tolerance using time-series prediction and dynamic modeling.Plant phenomics (Washington, D.C.) · 2025Article
- Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Plant phenomics (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential. However, there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass. Therefore, this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity. The leaf area index (LAI), soil plant analysis development (SPAD), and fractional vegetation cover were estimated from hyperspectral images, while plant height was estimated by a point cloud model from RGB images. Based on these 4 agronomic parameters, a total of 20 uniformity indices covering multiple growing stages were calculated. The changing trends in the uniformity indices were consistent with the results of visual interpretation. The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass. The results showed that Pielou's index of LAI had the strongest correlation with yield and biomass, with correlation coefficients of -0.760 and -0.801, respectively. The accuracies of the yield (coefficient of determination [
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Registered trials
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