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Deep-Pest-Detector: Automated Detection and Localization of Processionary Moth Nests on Pine Trees via Aerial Drones and Deep Neural Networks

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Applied Engineering in Agriculture. 38(2): 253-272. (doi: 10.13031/aea.14715) @2022
Authors:   Nael Jaber, Naseem Daher, Daniel Asmar
Keywords:   Aerial robotics, Agriculture, Detection, Machine learning, Multi-stream CNN, Pest control.


A deep learning architecture, which relies on multi-modal imagery from the RGB and thermal spectra, is proposed to detect PPM nests via aerial drones.

A GPS-based computation method is implemented to geo-tag the position of the detected nests in the drone‘s perimeter.

A Kalman filter is utilized as a nest tracker to avoid reporting back the position of the same nest several times.

The system is tested in a pine forest and exhibits accurate detection and localization, as evidenced by reporting back the positions of the detected PPM nests.

Abstract.The pine processionary moth (PPM) is considered the main defoliator of pine trees and is a menacing threat to various other perennial species including oak and cedar. Given their negative secondary effects, spraying of pesticides has been banned as a means for the eradication of PPM; instead, an individualized approach is to be adopted, in which each nest is localized and destroyed. Detection of PPM nests using optical sensing is challenging because of the changing outdoor lighting conditions and the camouflaged appearance of moth in the underlying foliage. This article proposes a promising solution for nest detection by fusing sensory data from an RGB camera on one hand, and a thermal camera on the other, both mounted on an aerial drone. The proposed detection system is built on a two-channeled deep convolutional neural network, one for each spectrum of the collected sensory data. Experiments performed in a pine forest demonstrate successful detection rates with an average accuracy of 97% in various experiments and settings. Geo-localization is performed to report the position of the detected nests within a scanned forest map by means of an estimation scheme that is particularly designed for this purpose achieving centimeter accuracy (<20 cm).

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