Early Detection of Red Palm Weevil Infestations using Deep Learning
  Classification of Acoustic Signals
Wadii Boulila Ayyub Alzahem Anis Koubaa Bilel Benjdira Adel Ammar
The Red Palm Weevil (RPW), also known as the palm weevil, is considered among
the world's most damaging insect pests of palms. Current detection techniques
include the detection of symptoms of RPW using visual or sound inspection and
chemical detection of volatile signatures generated by infested palm trees.
However, efficient detection of RPW diseases at an early stage is considered
one of the most challenging issues for cultivating date palms. In this paper,
an efficient approach to the early detection of RPW is proposed. The proposed
approach is based on RPW sound activities being recorded and analyzed. The
first step involves the conversion of sound data into images based on a
selected set of features. The second step involves the combination of images
from the same sound file but computed by different features into a single
image. The third step involves the application of different Deep Learning (DL)
techniques to classify resulting images into two classes: infested and not
infested. Experimental results show good performances of the proposed approach
for RPW detection using different DL techniques, namely MobileNetV2,
ResNet50V2, ResNet152V2, VGG16, VGG19, DenseNet121, DenseNet201, Xception, and
InceptionV3. The proposed approach outperformed existing techniques for public
datasets.