Testing early detection of pine processionary moth Thaumetopoea pityocampa nests using UAV-based methods

By

Garcia, André
Samalens, Jean-Charles
Grillet, Arnaud
Soares, Paula
Branco, Manuela
Van Halder, Inge
Jactel, Hervé
Battisti, Andrea

Type

Article

Date of Publication

2023

Original Publication

NeoBiota : advancing research on alien species and biological invasions

Volume

84

Pages

267--279

Subjects

AI algorithm , forest , Insecta , Lepidoptera , Notodontidae , Pest , PPM , UAV , urban

Contributed by

Pensoft Publishers

Language

English

Published by

Sophia, Bulgaria Pensoft Publishers

Identifiers

DOI: https://doi.org/10.3897/neobiota.84.95692

 

Abstract

Early detection of insect infestation is a key to the adoption of control measures appropriated to each local condition. The use of remote sensing was recommended for a quick scanning of large areas, although it does not work well with signals bearing low intensity or items that are difficult to detect. Unmanned Aerial Vehicle (UAV, or drone) may help in getting closer to individual trees and detect atypical signals of small dimensions. The larvae of the pine processionary moth (PPM, Thaumetopoea pityocampa (Denis & Schiffermüller, 1775, Lepidoptera, Notodontidae) build conspicuous silk nests on the external parts of the host plants at the beginning of the winter and their early detection may prompt managers to adopt management techniques. This work aims at testing two deep learning methods (Region-based Convolutional Neural Network - R-CNN and You Only Look Once - YOLO) to detect the nests under three different conditions of host plant species and forest stands in southern Europe. YOLO algorithm provided better results and it allowed us to achieve F1-scores as high as 0.826 and 0.696 for the detection of presence / absence and the individual nests, respectively. The detection of all the nests that can be present on a tree is not achievable with either UAV scanning or traditional ground observation, therefore the integration of the methods may allow the complete efficiency of the surveillance. The use of UAV combined with Artificial Intelligence (AI) image analysis is recommended for further use in forest and urban settings for the detection of the PPM nests. The recommended methods can be extended to other pest systems, especially when specific symptoms can be associated with an insect pest species.