Soybean ( Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion.

Abstract

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean ( Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients ( p 2 ) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

Plant phenotyping relevance

UAVマルチスペクトル画像からダイズ葉水分という植物生理形質を推定する手法を構築・検証しており、形質取得とモデル性能評価が研究の中心です。

Code and data availability

The supplied blocks describe UAV multispectral soybean leaf moisture data collection and ELM/XGBoost/BPNN modeling, but contain no data availability statement, no public dataset or image deposit, and no author code repository or URL. No paper-specific public asset is identified.

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