ArticleFrontiers in plant science2022
Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index.
Article in Frontiers in plant science, 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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12 citing papers in PubMed, 36 citations in OpenAlex.
- Improved leaf area index estimation in Jujube trees by fusing spatial-temporal features from UAV RGB time-series with a deep learning framework.Frontiers in plant science · 2026Article
- Improved Multi-Stage Rice Above-Ground Biomass Estimation Using Wavelet-Texture-Fused Vegetation Indices from UAV Remote Sensing.Plants (Basel, Switzerland) · 2025Article
- Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning.Plant methods · 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
- Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines.Frontiers in chemistry · 2025Review
- Monitoring Soybean Soil Moisture Content Based on UAV Multispectral and Thermal-Infrared Remote-Sensing Information Fusion.Plants (Basel, Switzerland) · 2024Article
- Spatio-temporal mapping of leaf area index in rice: spectral indices and multi-scale texture comparison derived from different sensors.Frontiers in plant science · 2024Article
- Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.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
- Construction of a Winter Wheat Comprehensive Growth Monitoring Index Based on a Fuzzy Degree Comprehensive Evaluation Model of Multispectral UAV Data.Sensors (Basel, Switzerland) · 2023Article
- Assessment of Soybean Lodging Using UAV Imagery and Machine Learning.Plants (Basel, Switzerland) · 2023Article
- Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models.Plant phenomics (Washington, D.C.) · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R
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