ArticleFrontiers in plant science2023
Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.
Article in Frontiers in plant science, 2023. 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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12 citing papers in PubMed.
- Methodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.Plants (Basel, Switzerland) · 2026Article
- Rice LAI estimation using UAV-based multi-parameter fusion at the booting stage.Scientific reports · 2026Article
- Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning.Frontiers in plant science · 2026Article
- Estimating maize canopy water content using UAV-based multispectral-thermal infrared imagery and canopy signal distributional features.Frontiers in plant science · 2026Article
- Prediction of growth parameters in the medicinal plantFrontiers in plant science · 2026Article
- Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models.Plants (Basel, Switzerland) · 2025Article
- The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features.Sensors (Basel, Switzerland) · 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
- Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.Frontiers in plant science · 2024Article
- The fusion of vegetation indices increases the accuracy of cotton leaf area prediction.Frontiers in plant science · 2024Article
- Maize Production under Drought Stress: Nutrient Supply, Yield Prediction.Plants (Basel, Switzerland) · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
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13 authors.
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Abstract
Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R
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