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.

翻译：红棕榈象甲（RPW），又称棕榈象甲，被认为是全球最具破坏性的棕榈科植物害虫之一。当前的检测技术包括通过视觉或声音检查检测红棕榈象甲的症状，以及通过化学检测被侵染棕榈树产生的挥发性特征。然而，在早期阶段有效检测红棕榈象甲病害被认为是种植椰枣树最具挑战性的问题之一。本文提出了一种高效的红棕榈象甲早期检测方法。该方法基于对红棕榈象甲声音活动的记录与分析。第一步，根据选定的特征集将声音数据转换为图像；第二步，将来自同一声音文件但由不同特征计算出的图像合并为一幅图像；第三步，应用不同的深度学习技术将生成的图像分为两类：受侵染和未受侵染。实验结果表明，该方法在使用不同深度学习技术（具体包括MobileNetV2、ResNet50V2、ResNet152V2、VGG16、VGG19、DenseNet121、DenseNet201、Xception和InceptionV3）进行红棕榈象甲检测时表现良好。该方法在公开数据集上的性能优于现有技术。