A Robust Recognition System for Pecan Weevil using Artificial Neural Networks

Abstract

Problem statement: Pecan Weevil is a widely found pest among pecan trees and these pests are known to cause significant damage to the pecan trees resulting in enormous annual losses to pecan growers. Traditional identification techniques for pecan weevil include traps with pheromones to detect the infestation of these pests. However, these traditional methods require expensive labor hours to set-up the traps and their monitoring. These techniques are also unreliable for early detection of pecan weevil infestation. Early detection of these pests is essential in minimizing the potential losses to the pecan trees. Approach: In this study, we develop a neural network-based identification system for pecan weevils. The neural networks require 3-9 image descriptors as input for successful recognition of pecan weevil. The nine image descriptors originate from standard image processing techniques such as Regional Properties (RP) and Zernike Moments (ZM). For training purposes, a comprehensive database was assembled comprising of 205 images of pecan weevil and 75 other insects commonly found in the same habitat. The networks were trained by two algorithms and several training ratios were studied to investigate the efficacy and robustness of the developed neural networks. Results: The neural networks developed in this study are capable of 100% recognition of pecan weevil as well as 100% recognition of other insects in the database. These recognition rates were achieved by using 75% of the data for training and using the Scaled Conjugate Gradient (SCG) algorithm and nine image descriptors as input. The average training times for these networks with the SCG algorithm was only 2-4 sec. and the testing time for a single image was only 0.16 sec. Conclusion: The neural network-based pecan weevil identification system developed in this study provides a reliable and robust method to identify pecan weevils and the proposed system should prove useful in designing an automated, wireless sensor network for detecting pecan weevil in the field.