A test of a pattern recognition system for identification of spiders

Do, Martin; Harp, Joel M.; Norris, K. C.

doi:10.1017/s0007485399000334

Cited by 58 publications

(56 citation statements)

References 11 publications

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“…Do, Harp and Norris [7] designed a computerized pattern recognition system for non-specialist in recognizing arachids and other arthropods to make the specimen identification easier and accurate. The researchers promote early insect pest detection in greenhouse crops in order to reduce pesticide use.…”

Section: Real Life Application Of Image Processing In the Fieldsmentioning

confidence: 99%

Pest Detection and Extraction Using Image Processing Techniques

Miranda¹,

Gerardo²,

Tanguilig³

2014

IJCCE

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Abstract-Detection of pests in the paddy fields is a major challenge in the field of agriculture, therefore effective measures should be developed to fight the infestation while minimizing the use of pesticides. The techniques of image analysis are extensively applied to agricultural science, and it provides maximum protection to crops, which can ultimately lead to better crop management and production. Monitoring of pests infestation relies on manpower, however automatic monitoring has been advancing in order to minimize human efforts and errors. This study extends the implementation of different image processing techniques to detect and extract insect pests by establishing an automated detection and extraction system for estimating pest densities in paddy fields. Experiment results shows that the proposed system provides a simple, efficient and fast solution in detecting pests in the rice fields.Index Terms-Automated pests management, image analysis object detection, object extraction.

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Section: Real Life Application Of Image Processing In the Fieldsmentioning

confidence: 99%

Pest Detection and Extraction Using Image Processing Techniques

Miranda¹,

Gerardo²,

Tanguilig³

2014

IJCCE

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“…ANN, however, have a great potential to partly automate the identification process, especially if coupled with image analysis Gaston & O'Neill 2004). This was also documented in the case study by Do et al (1999) who successfully applied ANN to identify six spider species from the family Lycosidae using transformed digital images of female genitalia. The ANN have also performed the treatment of bioacoustic data, e.g., Chesmore (2004) used them for automated identification of four species of British Orthoptera from sound recordings.…”

Section: Introductionmentioning

confidence: 86%

Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae)

et al. 2007

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Abstract:The classification methodology based on morphometric data and supervised artificial neural networks (ANN) was tested on five fly species of the parasitoid genera Tachina and Ectophasia (Diptera, Tachinidae). Objects were initially photographed, then digitalized; consequently the picture was scaled and measured by means of an image analyser. The 16 variables used for classification included length of different wing veins or their parts and width of antennal segments. The sex was found to have some influence on the data and was included in the study as another input variable. Better and reliable classification was obtained when data from both the right and left wings were entered, the data from one wing were however found to be sufficient. The prediction success (correct identification of unknown test samples) varied from 88 to 100% throughout the study depending especially on the number of specimens in the training set. Classification of the studied Diptera species using ANN is possible assuming a sufficiently high number (tens) of specimens of each species is available for the ANN training. The methodology proposed is quite general and can be applied for all biological objects where it is possible to define adequate diagnostic characters and create the appropriate database.

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“…Similar efforts have been made in the literature to develop recognition systems for other insects. For example, Liu et al (2009) developed an identification system for fruit flies, Arbuckle et al (2001) developed a system for identifying bees and Do et al (1999) developed an identification system for spiders. Recently, a recognition system has also been developed for red palm weevil (Al-Saqer and Hassan, 2011a;2011b).…”

Section: Introductionmentioning

confidence: 99%

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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A test of a pattern recognition system for identification of spiders

Cited by 58 publications

References 11 publications

Pest Detection and Extraction Using Image Processing Techniques

Pest Detection and Extraction Using Image Processing Techniques

Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae)

A Robust Recognition System for Pecan Weevil using Artificial Neural Networks

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