Detection of Wheat Powdery Mildew by Combined MVO_RF and Polarized Remote Sensing

Qijie Qian, Tianquan Liang (), Zibing Wu, Xinru Chen, Qingxin Tang and Quanzhou Yu
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Qijie Qian: School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
Tianquan Liang: School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
Zibing Wu: School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
Xinru Chen: School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
Qingxin Tang: School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
Quanzhou Yu: School of Geography and Environment, Liaocheng University, Liaocheng 252000, China

Agriculture, 2025, vol. 15, issue 21, 1-20

Abstract: Wheat powdery mildew poses a serious threat to crop growth and yield, highlighting the critical need for accurate detection to ensure food security and maintain agricultural productivity. This study explores the integration of polarization remote sensing with a Multi-Verse Optimizer (MVO)–enhanced Random Forest (RF) model for disease detection. Polarization imaging equipment was used to extract key polarization parameters, including the degree of polarization (DOP) and angle of polarization (AOP), from wheat leaves to capture subtle structural differences between healthy and diseased tissues. The MVO algorithm was employed to optimize RF hyperparameters, thereby improving classification performance and addressing the limitations of manual parameter tuning and conventional machine learning methods. Several machine learning algorithms were also evaluated for comparison. The results indicate that the proposed MVO_RF approach outperformed traditional methods, achieving an F1-score of 0.9715, a Kappa coefficient of 0.9797, and an overall accuracy of 0.9878. These findings demonstrate that the integration of polarization characteristics with MVO-optimized machine learning establishes a robust and efficient framework for monitoring wheat powdery mildew. More importantly, it facilitates early in-field disease warnings, enhances the accuracy and efficiency of targeted pesticide application, and offers quantitative decision-making support for smart agricultural management and disease prevention strategies.

Keywords: wheat powdery mildew; polarized remote sensing; degree of polarization; MVO_RF (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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