Early Detection of Red Palm Weevil Infestations using Deep Learning Classification of Acoustic Signals

TL;DR

This paper presents a deep learning-based acoustic signal analysis method for early detection of Red Palm Weevil infestations in palms, converting sound data into images and classifying them with various DL models, outperforming existing methods.

Contribution

It introduces a novel approach combining sound-to-image conversion and deep learning classification for early RPW detection, improving accuracy over previous techniques.

Findings

Deep learning models achieved high classification accuracy.

The approach outperformed existing detection methods.

Multiple DL architectures were effectively applied.

Abstract

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.