ArticlePlant methods2021
Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Leveraging UAV hyperspectral imaging for crop physiology and biochemistry: A comprehensive review of feature extraction and selection methods.Plant phenomics (Washington, D.C.) · 2026Review
- Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods.Sensors (Basel, Switzerland) · 2026Article
- Hyperspectral data set of stone cultural relics in high-precision machine vision scene.Scientific data · 2025Article
- Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models.Plants (Basel, Switzerland) · 2025Article
- Winter wheat yield prediction using UAV-based multivariate time series data and variate-independent tokenization.Plant phenomics (Washington, D.C.) · 2025Article
- Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale.BMC plant biology · 2025Article
- Non-destructive estimation of SPAD and biomass inFrontiers in plant science · 2025Article
- Evolving multispectral sensor configurations using genetic programming for estuary health monitoring.Journal of the Royal Society of New Zealand · 2025Article
- Estimating the full-period rice leaf area index using CNN-LSTM-Attention and multispectral images from unmanned aerial vehicles.Frontiers in plant science · 2025Article
- Research on Segmentation Method of Maize Seedling Plant Instances Based on UAV Multispectral Remote Sensing Images.Plants (Basel, Switzerland) · 2024Article
- A hyperspectral deep learning attention model for predicting lettuce chlorophyll content.Plant methods · 2024Article
- A dataset for estimating alfalfa leaf area and predicting leaf area index.Frontiers in plant science · 2024Article
- Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images.Frontiers in plant science · 2024Article
- Rapid evaluation of Radix Paeoniae Alba and its processed products by near-infrared spectroscopy combined with multivariate algorithms.Analytical and bioanalytical chemistry · 2023Article
- Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.Frontiers in plant science · 2023Article
- Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage.Frontiers in plant science · 2023Article
- Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning.Plant methods · 2022Article
- Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.Frontiers in plant science · 2022Article
- Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning.Frontiers in plant science · 2022Article
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7 authors.
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Abstract
backgroundTo accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture.
methodsThe UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.
resultsThe results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.
conclusionsThe Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
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