Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery

Abstract Background Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Ve...

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Main Authors: Shuang Wu, Lei Deng, Lijie Guo, Yanjie Wu
Format: Article
Language:English
Published: BMC 2022-05-01
Series:Plant Methods
Subjects:
Online Access:https://doi.org/10.1186/s13007-022-00899-7
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author Shuang Wu
Lei Deng
Lijie Guo
Yanjie Wu
author_facet Shuang Wu
Lei Deng
Lijie Guo
Yanjie Wu
author_sort Shuang Wu
collection DOAJ
description Abstract Background Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion. Methods To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression. Results The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat. Conclusions The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
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spelling doaj.art-2231e4b4a27a4468ba40742259018acb2022-12-22T00:24:05ZengBMCPlant Methods1746-48112022-05-0118111610.1186/s13007-022-00899-7Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imageryShuang Wu0Lei Deng1Lijie Guo2Yanjie Wu3College of Resource Environment and Tourism, Capital Normal UniversityCollege of Resource Environment and Tourism, Capital Normal UniversityCollege of Resource Environment and Tourism, Capital Normal UniversityCollege of Resource Environment and Tourism, Capital Normal UniversityAbstract Background Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion. Methods To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression. Results The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat. Conclusions The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.https://doi.org/10.1186/s13007-022-00899-7Leaf area index (LAI)Unmanned aerial vehicle (UAV)High resolutionData fusion
spellingShingle Shuang Wu
Lei Deng
Lijie Guo
Yanjie Wu
Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery
Plant Methods
Leaf area index (LAI)
Unmanned aerial vehicle (UAV)
High resolution
Data fusion
title Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery
title_full Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery
title_fullStr Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery
title_full_unstemmed Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery
title_short Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery
title_sort wheat leaf area index prediction using data fusion based on high resolution unmanned aerial vehicle imagery
topic Leaf area index (LAI)
Unmanned aerial vehicle (UAV)
High resolution
Data fusion
url https://doi.org/10.1186/s13007-022-00899-7
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AT leideng wheatleafareaindexpredictionusingdatafusionbasedonhighresolutionunmannedaerialvehicleimagery
AT lijieguo wheatleafareaindexpredictionusingdatafusionbasedonhighresolutionunmannedaerialvehicleimagery
AT yanjiewu wheatleafareaindexpredictionusingdatafusionbasedonhighresolutionunmannedaerialvehicleimagery