  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive
insect pest of global importance that damages several crops, compromising
agri-food production. Field monitoring procedures are fundamental to perform
risk assessment operations, in order to promptly face crop infestations and
avoid economical losses. To improve pest management, spectral cameras mounted
on Unmanned Aerial Vehicles (UAVs) and other Internet of Things (IoT) devices,
such as smart traps or unmanned ground vehicles, could be used as an innovative
technology allowing fast, efficient and real-time monitoring of insect
infestations. The present study consists in a preliminary evaluation at the
laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible
technology to detect BMSB specimens on different vegetal backgrounds,
overcoming the problem of BMSB mimicry. Hyperspectral images of BMSB were
acquired in the 980-1660 nm range, considering different vegetal backgrounds
selected to mimic a real field application scene. Classification models were
obtained following two different chemometric approaches. The first approach was
focused on modelling spectral information and selecting relevant spectral
regions for discrimination by means of sparse-based variable selection coupled
with Soft Partial Least Squares Discriminant Analysis (s-Soft PLS-DA)
classification algorithm. The second approach was based on modelling spatial
and spectral features contained in the hyperspectral images using Convolutional
Neural Networks (CNN). Finally, to further improve BMSB detection ability, the
two strategies were merged, considering only the spectral regions selected by
s-Soft PLS-DA for CNN modelling.
