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 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.

Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

“…The RF model performed better than the SVR model in the three machine learning models, while the BP model was the weakest in all three types of data inputs. This was consistent with the view of Wu et al (2022), who used random forest, support vector machine and multiple linear regression techniques to fuse spectral, structural and thermal characteristics of the canopy to estimate LAI in wheat, and revealed that regardless of which features were fused, RF performed best in LAI prediction. Zha et al (2020) also found that RF was superior to SVR, MLR and ANN in predicting nitrogen content in rice using spectral features.…”

“…Unmanned Aerial Vehicle (UAV) remote sensing has rapidly evolved into a crucial tool for agricultural monitoring, driven by its advantages of low cost, high flexibility, and the ability to acquire high temporal and spatial resolution data on demand (Lan et al, 2020;Yan et al, 2019;Yang et al, 2023). Various remote sensing data, including hyperspectral, multispectral, and thermal infrared, are widely used for estimating crop parameters such as leaf area index (Liang et al, 2015;Roth and Streit, 2018;Wu et al, 2022), leaf chlorophyll content (Borhan et al, 2004;Daughtry et al, 2000;Jena, 2017), above-ground biomass (Eckert, 2012;Liu et al, 2019;, and so on.…”

“…For examples, Yang et al (2021) To incorporate the use of information from UAV remote sensing data, researchers explored different regression methods to determine quantitative relationships between remote sensing data variables and crop growth parameters. These regression methods can be classified into two main groups: (i) traditional linear regression techniques, including multiple linear regression (Wu et al, 2022), partial least squares regression (Rischbeck et al, 2016), ridge regression (Ma et al, 2022), etc. ;and (ii) machine learning algorithms covering back propagation neural networks (Jiang et al, 2022), random forests , and support vector regression (Li et al, 2022).…”

Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data

“…This advantage guarantees additional information for data fusion with the vegetation indices as full explanations. According to Wu et al [26], the combination of UAV-based multi-sensor synchronous observation data products can bring higher accuracy in the prediction process.…”

“…More recently, small-sized unmanned aerial vehicles (UAVs) brought the capability to acquire remote-sensed data from different sensors with high spatial resolution and good temporal flexibility [22]. Studies were performed using UAV-based imagery to correlate the LAI in vineyards [10,[23][24][25][26], olives [27], wheat [28], maize [29,30], mangrove forests [15], and rice [31,32].…”