A Robust Recognition System for Pecan Weevil using Artificial Neural Networks

Abstract

Problem statement: Pecan Weevil is a widely found pest among pecan tr ees and these pests are known to cause significant damage to the pecan trees resulting in enormous annual losses to pecan growers. Traditional identification technique s for pecan weevil include traps with pheromones to detect the infestation of these pests. However, the se traditional methods require expensive labor hour s to set-up the traps and their monitoring. These tec hniques are also unreliable for early detection of pecan weevil infestation. Early detection of these pests is essential in minimizing the potential loss es to the pecan trees. Approach: In this study, we develop a neural network-based i dentification system for pecan weevils. The neural networks require 3-9 image descriptors as input for successful recognition of pecan weevil. The nine image descriptors origina te 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 traine d by two algorithms and several training ratios were studied to investigate the efficacy and robust ness 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 Conju gate Gradient (SCG) algorithm and nine image descriptors as input. The average training times fo r 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 s hould prove useful in designing an automated, wireless sensor network for detecting pecan weevil in the field.