| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 1.05 MB | Adobe PDF |
Advisor(s)
Abstract(s)
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.
Description
Keywords
Almond orchard Vegetation indices Machine learning Support vector machine
Citation
Guimarães, Nathalie; Pádua, Luís; Sousa, Joaquim J.; Bento, Albino; Couto, Pedro (2023). Identification of aphids using machine learning classifiers on UAV-based multispectral data. 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). p. 3462-3465. ISBN 979-8-3503-2010-7
Publisher
IEEE