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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.

Abstract Background Malaria control in Panama is problematic due to the high diversity of morphologically similar Anopheles mosquito species, which makes identification of vectors of human Plasmodium challenging. Strategies by Panamanian health authorities to bring malaria under control targeting Anopheles vectors could be ineffective if they tackle a misidentified species. Methods A rapid mass spectrometry identification procedure was developed to accurately and timely sort out field-collected Neotropical Anopheles mosquitoes into vector and non-vector species. Matrix-assisted laser desorption/ionization (MALDI) mass spectra of highly-abundant proteins were generated from laboratory-reared mosquitoes using different extraction protocols, body parts, and sexes to minimize the amount of material from specimen vouchers needed and optimize the protocol for taxonomic identification. Subsequently, the mass spectra of field-collected Neotropical Anopheles mosquito species were classified using a combination of custom-made unsupervised (i.e., Principal component analysis—PCA) and supervised (i.e., Linear discriminant analysis—LDA) classification algorithms. Results Regardless of the protocol used or the mosquito species and sex, the legs contained the least intra-specific variability with enough well-preserved proteins to differentiate among distinct biological species, consistent with previous literature. After minimizing the amount of material needed from the voucher, one leg was enough to produce reliable spectra between specimens. Further, both PCA and LDA were able to classify up to 12 mosquito species, from different subgenera and seven geographically spread localities across Panama using mass spectra from one leg pair. LDA demonstrated high discriminatory power and consistency, with validation and cross-validation positive identification rates above 93% at the species level. Conclusion The selected sample processing procedure can be used to identify field-collected Anopheles species, including vectors of Plasmodium, in a short period of time, with a minimal amount of tissue and without the need of an expert mosquito taxonomist. This strategy to analyse protein spectra overcomes the drawbacks of working without a reference library to classify unknown samples. Finally, this MALDI approach can aid ongoing malaria eradication efforts in Panama and other countries with large number of mosquito’s species by improving vector surveillance in epidemic-prone sites such as indigenous Comarcas.