Artificial Neural Networks Based Red Palm Weevil (&lt;i&gt;Rynchophorus Ferrugineous, Olivier&lt;/i&gt;) Recognition System

Al-Saqer,

doi:10.3844/ajabssp.2011.356.364

Cited by 22 publications

(19 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lab-based In lab-based setting (see examples on Figure 2a and Figure 2b), most of the acquisition systems are manually manipulated. In [61,36,37,39,38,40,41,42,43,44,57,50,58] the acquisition is made manually on a microscope slide, with some standard illumination systems (fiber optic light sources, desk lamps, etc.). In [61], to change the pose of the insect, an interesting trick has been used body rotation was achieved by first placing two short nylon fishing lines on either side of the specimen and gently moving a cover slip placed over them by using the tip of the index finger.…”

Section: Category Levels Refsmentioning

confidence: 99%

A survey on image-based insect classification

Martineau

Conte

Raveaux

et al. 2017

Pattern Recognition

183

View full text Add to dashboard Cite

International audienceEntomology has had many applications in many biological domains (i.e insect counting as a biodiversity index). To meet a growing biological demand and to compensate a decreasing workforce amount, automated entomology has been around for decades. This challenge has been tackled by computer scientists as well as by biologists themselves. This survey investigates fourty-four studies on this topic and tries to give a global picture on what are the scientific locks and how the problem was addressed. Views are adopted on image capture, feature extraction, classification methods and the tested datasets. A general discussion is finally given on the questions that might still remain unsolved such as: the image capture conditions mandatory to good recognition performance, the definition of the problem and whether computer scientist should consider it as a problem in its own or just as an instance of a wider image recognition problem

show abstract

Section: Category Levels Refsmentioning

confidence: 99%

A survey on image-based insect classification

Martineau

Conte

Raveaux

et al. 2017

Pattern Recognition

183

View full text Add to dashboard Cite

show abstract

“…Then, studies began to emerge using standard local and global features such as SIFT [18]. On top of these standard handcrafted features are learnt higher level features using MLPs [1], Bag of Words [12] or Sparse Coding [19]. The next step is to introduce hierarchically learnt descriptors, with many levels of abstraction.…”

Section: State Of the Artmentioning

confidence: 99%

“…The observation that can be done is the features are no more particular to the problem itself and is about learning hierarchical representations to get satisfying feature spaces that suit the biologists goals through learning on their images. In such a frame, [1] applies MLPs on raw pixel even though the problem is a very simple one (two classes : harmful/harmless insects).…”

Section: State Of the Artmentioning

confidence: 99%

Effective Training of Convolutional Neural Networks for Insect Image Recognition

Martineau

Raveaux

Chatelain

et al. 2018

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

Insects are living beings whose utility is critical in life sciences. They enable biologists obtaining knowledge on natural landscapes (for example on their health). Nevertheless, insect identification is timeconsuming and requires experienced workforce. To ease this task, we propose to turn it into an image-based pattern recognition problem by recognizing the insect from a photo. In this paper state-of-art deep convolutional architectures are used to tackle this problem. However, a limitation to the use of deep CNNs is the lack of data and the discrepancies in classes cardinality. To deal with such limitations, transfer learning is used to apply knowledge learnt from ImageNet-1000 recognition task to insect image recognition task. A question arises from transfer-learning : is it relevant to retrain the entire network or is it better not to modify some layers weights? The hypothesis behind this question is that there must be part of the network which contains generic (problem-independent) knowledge and the other one contains problem-specific knowledge. Tests have been conducted on two different insect image datasets. VGG-16 models were adapted to be more easily learnt. VGG-16 models were trained a) from scratch b) from ImageNet-1000. An advanced study was led on one of the datasets in which the influences on performance of two parameters were investigated: 1) The amount of learning data 2) The number of layers to be finetuned. It was determined VGG-16 last block is enough to be relearnt. We have made the code of our experiment as well as the script for generating an annotated insect dataset from ImageNet publicly available.

show abstract

“…Few approaches have already been developed such as Surveillance of Fruit Flies System for identification of Fruit Flies (Liu et al, 2009), Automated Bee Identification System for identification of Bees (Arbuckle et al, 2001), Species Identification Automated and Web Accessible System for identification of Spiders (Do et al, 1999), Red Palm Weevil Recognition System for identification of Red Palm Weevil (Al-Saqer and Hassan, 2011a;2011b).…”

Section: Introductionmentioning

confidence: 99%

Pecan Weevil Recognition Using Support Vector Machine Method

Hassan¹,

Al-Saqer²

2011

American Journal of Agricultural and Biological Sciences

View full text Add to dashboard Cite

Problem statement: The Pecan weevil was considered as the most dangerous pest of Pecan fruits. The aim of this research is to evaluate Support Vector Machine method (SVM) for identifying Pecan Weevil among other insects. Eventually, this recognition system will serve in a wireless imaging network for monitoring Pecan Weevils. Approach: SVM has been evaluated using two different kernel functions i.e., Polynomial Function and Radial Basis Function. Database of 205 Pecan Weevils and 75 other insects which typically exist in pecan habitat has been used. Three sets of input data for SVM have been generated by two standard region-based recognition methods. These sets are comprised of output obtained by Zernike Moments, Regional Properties and combination of these two methods. For each kernel function, the system had been trained by 25, 50 and 75% of data and remaining ratio in each case has been used for testing. Each experiment is repeated ten times and average results are considered for comparisons and analysis. Results: The optimum recognition rate had been found when system is trained by 75% of data. The results are approximately similar when the input data is obtained by Regional Properties and combination of Regional Properties and Zernike Moments methods. The optimum results are obtained when input data has been obtained by Zernike Moments alone for lower values of sigma 'σ'. The proposed system is able to successfully recognize 99% of Pecan Weevil and 97% of the other insects using the radial basis function. The proposed system took approximately 31 sec for processing 75% of the data which include the time for training. The testing time is found to be 0.15 sec. Conclusion: Promising results can be obtained when input data is obtained by Zernike Moments and SVM is trained by RBF and 75% of data.

show abstract

Artificial Neural Networks Based Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition System

Abstract: Problem statement:The most dangerous insect for the existence of palm trees

Cited by 22 publications

References 26 publications

A survey on image-based insect classification

A survey on image-based insect classification

Effective Training of Convolutional Neural Networks for Insect Image Recognition

Pecan Weevil Recognition Using Support Vector Machine Method

Contact Info

Product

Resources

About