Classification of airborne multispectral imagery to quantify common vole impacts on an agricultural field

Abstract

Abstract BACKGROUND The common vole ( Microtus arvalis ) is a very destructive agricultural pest. Particularly in Europe, its monitoring is essential not only for adequate management and outbreak forecasting, but also for accurately determining the vole's impact on affected fields. In this study, several alternatives for estimating the damage to alfalfa fields by voles through unmanned vehicle systems (UASs) and multispectral cameras are presented. Currently, both the farmers and agencies involved in the integrated pest management (IPM) programs of voles do not have sufficiently precise methods for accurate assessments of the real impact to crops. RESULTS Overall, the four multispectral classification methods presented showed similar performances. However, the normalized difference vegetation index (NDVI)‐based segmentation exhibited the most accurate and reliable appraisal of the affected areas. Nevertheless, it must be noted that the simplest method, which was based on an automatic classification, provided results similar to those obtained by more complex methods. In addition, a significant direct relationship was found between the number of active burrows and damage to the alfalfa canopy. CONCLUSION Unmanned vehicle systems, combined with multispectral imagery classification, are an effective and easily transferable methodology for the assessment and monitoring of common vole damage to agricultural plots. This combination of methods facilitates decision‐making processes for IPM control strategies against this pest. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Plant phenotyping relevance

UAS multispectral画像の分類法を比較し、アルファルファの被害面積・キャノピー損傷という植物状態を評価する方法が中心的に検討されている。

Code and data availability

The article describes UAS multispectral imagery, field validation points, and classification workflows (NDVI segmentation, SVM, ISODATA, OORF) performed in ArcGIS Pro and SPSS, but contains no data availability statement, no public repository deposit of imagery, field measurements, or analysis code, and no author-proc.

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