Early Detection of Potential Infestation by Capnodis tenebrionis (L.) (Coleoptera: Buprestidae), in Stone and Pome Fruit Orchards, Using Multispectral Data from a UAV

Abstract

Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models.

Plant phenotyping relevance

UAVマルチスペクトル画像から樹冠面積・植生指数などの樹木状態を抽出し、害虫による植物症状を予測する手法が研究の中心であるため。

Code and data availability

The supplied blocks describe UAV multispectral data collection, NDVI/NDRE computation in QGIS, and machine-learning models in R, but contain no data availability statement, repository deposit, or public URL for the imagery, per-tree phenotype tables, or analysis code. Only generic software (Metashape, QGIS, R packages)

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