A Reliable Identification System for Red Palm Weevil

Abstract

Problem statement: Red Palm Weevil (RPW) is a widely found pest among palm trees and is known to cause significant losses every year to palm growers. Existing identification techniques for RPW comprise of using traps with pheromones to detect these pests. However, these traditional methods are labor-intensive, expensive to implement and unreliable for early detection of RPW infestation. Early detection of these pests would provide the best opportunity to eradicate them and minimize the potential losses of palm trees. Approach: In this study, a reliable identification system is developed to identify RPW by using only a small number of image descriptors in combination with neural network models. The neural networks were developed by using between three to nine image descriptors as inputs and a large database of insects’ images was used for training. Three different training ratios ranging from 25-75% were used and the network was trained by two different algorithms. Further, several scenarios were formulated to test the efficacy and reliability of the newly developed identification system. Results: The results indicate that the identification system developed in this study is capable of 100% recognition of RPW and 93% recognition of other insects in the database by taking as input only three easily-calculable image descriptors. Further, the average training times for these networks was 13 sec and the testing time for a single image was only 0.015 sec. Conclusion: The new system developed in this study provided reliable identification for RPW and was found to be up to 14 times faster in training and three times faster in testing of insects’ images.