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An Innovative Method of Monitoring Cotton Aphid Infestation Based on Data Fusion and Multi-Source Remote Sensing Using Unmanned Aerial Vehicles

文献类型： 外文期刊

作者： Ren, Chenning 1 ; Liu, Bo 1 ; Liang, Zhi 1 ; Lin, Zhonglong 1 ; Wang, Wei 2 ; Wei, Xinzheng 3 ; Li, Xiaojuan 1 ; Zou, Xiangjun 1 ;

作者机构： 1.Xinjiang Univ, Coll Mech Engn, Urumqi 830017, Peoples R China

2.Xinjiang Acad Agr Sci, Inst Plant Protect, Key Lab Integrated Pest Management Crop Northweste, Minist Agr & Rural Affairs, Urumqi 830091, Peoples R China

3.Xinjiang Uygur Autonomous Reg Plant Protect & Quar, Urumqi 830006, Peoples R China

关键词： cotton aphid; UAV remote sensing; pest monitoring; spectral feature fusion; machine learning

期刊名称：DRONES （ 影响因子：4.8； 五年影响因子：5.0 ）

ISSN：

年卷期： 2025 年 9 卷 4 期

页码：

收录情况： SCI

摘要： Cotton aphids are the primary pests that adversely affect cotton growth, and they also transmit a variety of viral diseases, seriously threatening cotton yield and quality. Although the traditional remote sensing method with a single data source improves the monitoring efficiency to a certain extent, it has limitations with regard to reflecting the complex distribution characteristics of aphid pests and accurate identification. Accordingly, there is a pressing need for efficient and high-precision UAV remote sensing technology for effective identification and localization. To address the above problems, this study began by presenting a fusion of two kinds of images, namely panchromatic and multispectral images, using Gram-Schmidt image fusion technique to extract multiple vegetation indices and analyze their correlation with aphid damage indices. After fusing the panchromatic and multispectral images, the correlation between vegetation indices and aphid infestation degree was significantly improved, which could more accurately reflect the spatial distribution characteristics of aphid infestation. Subsequently, these machine learning techniques were applied for modeling and evaluation of the performance of multispectral and fused image data. The results of the validation revealed that the GBDT (Gradient-Boosting Decision Tree) model for GLI, RVI, DVI, and SAVI vegetation indices based on the fused data performed the best, with an estimation accuracy of R2 of 0.88 and an RMSE of 0.0918, which was obviously better than that of the other five models, and that the monitoring method of combining fusion of panchromatic and multispectral imagery with the accuracy and efficiency of the GBDT model were noticeably higher than those of single multispectral imaging. The fused panchromatic and multispectral images combined with the GBDT model significantly outperformed the single multispectral image in terms of precision and efficiency. In conclusion, this study demonstrated the effectiveness of image fusion combined with GBDT modeling in cotton aphid pest monitoring.

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