Ion beam modification of advanced wide bandgap semiconductor hetero- and nanostructures

Funder

Organizational Unit

Publications

Identification of aphids using machine learning classifiers on UAV-based multispectral data

Publication . Guimarães, Nathalie; Pádua, Luís; Sousa, Joaquim J.; Bento, Albino; Couto, Pedro

Almond trees in Portugal are susceptible to aphid infestation, which can result in reduced fruit production. To effectively tackle this issue, the combination of remote sensing (RS) data and machine learning (ML) classifiers can be used to accurately detect the presence of aphids. This study focuses in the implementation of ML classifiers and RS data analysis to identify aphids on almond trees, using high-resolution multispectral data collected through an unmanned aerial vehicle (UAV) in a Portuguese almond orchard. Four ML classifiers, kNN, SVM, RF and XGBoost, were employed and fine-tuned using vegetation indices derived from spectral data. The results revealed that the SVM classifier achieved an overall accuracy (OA) of 77%, followed by kNN with an OA of 74%, while XGBoost and RF achieved OAs of 71% and 69%, respectively. Consequently, this study demonstrates the viability of employing RS data and ML classifiers for aphid identification in almond orchards.

2023Conference paper

Open access