Identification of Pecan Weevils through Image Processing

Al-Saqer,

doi:10.3844/ajabssp.2011.69.79

Cited by 14 publications

(15 citation statements)

References 11 publications

(13 reference statements)

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“…Table 2 lists the summary results obtained with the neural networks in this study. The table also compares the results obtained in this study with our earlier work where template matching (Al-Saqer et al, 2010) and support vector machine method (Al-Saqer and Hassan, 2011c) were used to identify pecan weevil. As evident from Table 2, the current study provides the best results in terms of recognition of pecan weevil.…”

Section: Resultsmentioning

confidence: 64%

“…In that study, it was shown that regional properties and Zernike moments were sufficient to successfully recognize the pecan weevil. However, only 15% of the pecan weevil images were used for testing and the two recognition methods had to be used together (Al-Saqer et al, 2010). In another study (Al-Saqer and Hassan, 2011c), a system based on the Support Vector Machine (SVM) method was used for recognition of pecan weevil and promising results were obtained.…”

Section: Introductionmentioning

confidence: 99%

“…An identification system for pecan weevils was recently proposed by Al-Saqer et al (2010) that utilized image processing techniques based on the template matching method (Ashaghathra, 2008). In that study, it was shown that regional properties and Zernike moments were sufficient to successfully recognize the pecan weevil.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Recognition System for Pecan Weevil using Artificial Neural Networks

Al-Saqer¹

2012

American Journal of Applied Sciences

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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 networkbased 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.

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Section: Resultsmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

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A Robust Recognition System for Pecan Weevil using Artificial Neural Networks

Al-Saqer¹

2012

American Journal of Applied Sciences

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“…Euclidean distance formula has been used to find distance between the two superpixel nodes (Sayeed et al, 2009;Abas and Ono, 2010;Odeh et al, 2009;Jusoff, 2010;Al-Haddad et al, 2009;Al-Saqer et al, 2010;Nazif and Lee, 2010;Sleit et al, 2009;Moghaddasi et al, 2009). This formula is simple and easy to calculate distance between the nodes.It is represented as dis:…”

Section: Similarity Dependency Matrixmentioning

confidence: 99%

Object Recognition Based on Image Segmentation and Clustering

Thilagamani¹

2011

Journal of Computer Science

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Problem statement: This study deals with object recognition based on image segmentation and clustering. Acquiring prior information of an image is done via two separate processes. Approach: The first process deals with detecting object parts of an image and integration of detected parts into several clusters. All these cluster centers form the visual words. The second process deals with over segmenting the image into super pixels and formation of larger sub region using Mid-level clustering algorithm, since it incorporates various information to decide the homogeneity of a sub region. Results: The outcome of the two processes are used for the similarity graph representation for object segmentation as proposed. In order to model the relationship between the shape and color or texture matrix representation has been used. Mask map ensures that the probability of each super pixel to harp inside an object. Conclusion: The basic whim is to integrate all the priors into an uniform framework. Thus the ORBISC can handle size, color, texture and pose variations better than those methods that focus on the objects only.

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“…Some automated systems for identification and recognition of different insects were found in literature, such as Automated Bee Identification System (ABIS) by Arbuckle et al (2001) for identification of Bees; Digital Automated Identification System (DAISY) by Watson et al (2004) for identification of Ophioninae; Automated Insect Identification through Concatenated Histograms of Local Appearance System (AIICHLA) by Larios et al (2007) for identification of Stonefly larvae; Species Identification Automated and Web Accessible System (SPIWA) by Do et al (1999) for identification of Spiders); a software system developed by Al-Saqer et al (2010) for identification of Pecan Weevil. Agri.…”

mentioning

confidence: 99%