AI-Powered Early Warning for Red Palm Weevil Infestations

Abstract

The red palm weevil (Rhynchophorus ferrugineus) is a pest known for infesting and causing harm to palm trees. Symptoms of red palm weevil infection include leaf wilting, browning of the palms, exit holes in the trunk and general weakening of the tree. These symptoms only appear after full spread within the palm and in the final stages of infection, when rescuing the palm becomes challenging and control the pest. Early detection is considered the best methods for combating the red palm weevil.
The primary goal of this study aims to design a system for early detection of this pest using deep learning techniques. Audio signals from palm trees were converted into spectrograms, and a 6-layer convolutional neural network (CNN) model was trained on a dataset of audio recordings using MATLAB. The model achieved a high accuracy of 99.78% in classifying infested and healthy palm trees. The results show that deep learning May serve as a valuable method for detecting early signs of red palm weevil infestations, contributing to the control of this pest and the preservation of palm