Affiliation:
1. Laboratory of Agricultural Zoology and Entomology, Agricultural University of Athens, 118 55 Athina, Greece
2. Laboratory of Sericulture and Apiculture, Agricultural University of Athens, 118 55 Athina, Greece
3. Laboratory of Plant Breeding and Biometry, Agricultural University of Athens, 118 55 Athina, Greece
4. Department of Computer Science and Engineering, European University of Cyprus, Nicosia 2404, Cyprus
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.
Funder
THE RESEARCH AND INNOVATION FOUNDATION OF CYPRUS
Subject
Agronomy and Crop Science
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