Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images.

Abstract

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

Plant phenotyping relevance

UAVマルチスペクトル画像からピーナッツのLAIを推定するモデルを開発・較正し、精度比較しており、植物形質取得手法が研究の中心である。

Code and data availability

The article describes UAV multispectral peanut LAI measurements and BPN/regression modeling, but contains no public dataset deposit, no author code/model availability statement, and no supplementary data link. The only URL present is the CC BY license notice, which is not a paper-specific asset.

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