UAV multi-source data fusion with super-resolution for accurate soybean leaf area index estimation.

Abstract

Introduction The Leaf Area Index (LAI) is a critical biophysical parameter for assessing crop canopy structure and health. Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors offer a high-throughput solution for LAI estimation, but flight altitude compromises between efficiency and image resolution, ultimately impacting accuracy. This study investigates the integration of super-resolution (SR) image reconstruction with multi-sensor data to enhance LAI estimation for soybeans across varying UAV flight altitudes. Methods RGB and multispectral images were captured at four flight altitudes: 15 m, 30 m, 45 m, and 60 m. The acquired images were processed using several SR algorithms (SwinIR, Real-ESRGAN, SRCNN, and EDSR). Texture features were extracted from the RGB images, and LAI estimation models were developed using the XGBoost algorithm, testing data fusion strategies that included RGB-only, multispectral-only, and a combined RGB-multispectral approach. Results (1) SR performance declined with increasing altitude, with SwinIR achieving superior image reconstruction quality (PSNR and SSIM) over other methods. (2) Texture features from RGB images showed strong sensitivity to LAI. The XGBoost model leveraging fused RGB and multispectral data achieved the highest accuracy (relative error: 4.16%), outperforming models using only RGB (5.25%) or only multispectral data (9.17%). (3) The application of SR techniques significantly improved model accuracy at 30 m and 45 m altitudes. At 30 m, models incorporating Real-ESRGAN and SwinIR achieved an average R 2 of 0.86, while at 45 m, these methods yielded models with an average R 2 of 0.77. Discussion The results demonstrate that the fusion of SR-reconstructed imagery with multi-sensor data can effectively mitigate the negative impact of higher flight altitudes on LAI estimation accuracy. This approach provides a robust and efficient framework for UAV-based crop monitoring, enhancing data-driven decision-making in precision agriculture.

Plant phenotyping relevance

UAV画像の超解像・マルチセンサー融合・機械学習を用いて、植物キャノピー形質であるダイズLAIの推定手法を開発・評価しており、フェノタイピング手法が中心です。

Code and data availability

The article describes UAV RGB/multispectral soybean imagery, SR processing, and XGBoost LAI modeling, but provides no explicit public deposit, URL, or accession for its datasets, images, or analysis code. The data availability statement is generic ('can be found in online repositories... names... can be found in the文章/

No evidence-backed public reproduction asset is currently recorded.