ArticlePlant phenomics (Washington, D.C.)2022
Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning.
Article in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Synchronized UAV multi-angle inversion of canopy structure parameters in wheat breeding materials.Plant phenomics (Washington, D.C.) · 2026Article
- Next-generation translational genomics for developing future crops.Functional & integrative genomics · 2025Review
- 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
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques.Plant phenomics (Washington, D.C.) · 2024Article
- UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation.Plant phenomics (Washington, D.C.) · 2024Article
- Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework.Plant phenomics (Washington, D.C.) · 2024Article
- Soybean leaf estimation based on RGB images and machine learning methods.Plant methods · 2023Article
- A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background.Plant phenomics (Washington, D.C.) · 2023Article
- Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models.Plant phenomics (Washington, D.C.) · 2023Article
- Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.Journal of experimental botany · 2022Article
- Retrieving rice (Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI<2 due to background effect, which can be addressed by applying background correction on original reflectance map based on vegetation-background classification. Overall, RFR models achieved accurate LAI prediction from background-corrected reflectance for Exp16 (correlation coefficient (
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