Construction of a Winter Wheat Comprehensive Growth Monitoring Index Based on a Fuzzy Degree Comprehensive Evaluation Model of Multispectral UAV Data.

Abstract

Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGI ewm ) and FCE (CGI fce ) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGI fce and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination ( R 2 ) values of ELM-CGI ewm and ELM- CGI fce increased by 20.83% and 20.37%, respectively. (3) The CGI fce inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGI fce ), with R 2 reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGI fce (PSO-ELM-CGI fce ), and the precision is significantly improved compared with that before optimization, with R 2 reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring.

Plant phenotyping relevance

UAVマルチスペクトル画像から冬小麦の総合生育指標を推定する計測・特徴抽出・機械学習ワークフローが研究の中心であり、植物形質の推定手法を開発・比較している。

Code and data availability

The supplied blocks describe UAV multispectral imagery, ground measurements (AGB, SPAD, LWC), and MATLAB-based modeling, but contain no data availability statement, no public dataset deposit, and no author code/model release. No paper-specific public asset is identified.

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