This document summarizes recent advances in insect taxonomy techniques. It outlines 9 different approaches: 1) image-based recognition using feature extraction and classification, 2) colour histogram and grey-level co-occurrence matrix (GLCM) analysis of wing images, 3) pattern recognition techniques, 4) extension theory using matter-element matrices of image features, 5) stacked spatial-pyramid kernels to boost classifier performance, 6) ontology-based recognition using visual and taxonomic ontologies, 7) K-nearest neighbors spectral regression and linear discriminant analysis of face-like features, 8) histogram of local appearances features using bag-of-words models, and 9) a hybrid approach using discriminative local soft coding and multiple kernel learning.
Animals are classified into the animal kingdom. Each kingdom is then further divided into increasingly smaller groups based on similarities. The taxonomists names different levels of groups. The development of insects classification gets further advancement when compared to the earlier classification.
the presentation will help you learn more about how the insect eyes really work in field conditions and more over for the better understanding you can take help from from book: THE INSECTS:STRUCTURE AND FUNCTION byR.F.CHAPMAN.....as the contents of my presentation are from that book only.....
Animals are classified into the animal kingdom. Each kingdom is then further divided into increasingly smaller groups based on similarities. The taxonomists names different levels of groups. The development of insects classification gets further advancement when compared to the earlier classification.
the presentation will help you learn more about how the insect eyes really work in field conditions and more over for the better understanding you can take help from from book: THE INSECTS:STRUCTURE AND FUNCTION byR.F.CHAPMAN.....as the contents of my presentation are from that book only.....
Importance of study of immature stages of insects in agricultureSanju Thorat
The type of life cycle will vary with the insect-pest. However, most pests have certain weak points during their life cycle when they are the most vulnerable to manage. Some insect are predators, either as larvae or in both larval and adult stages. The decomposition of organic waste, such as dung and manures are an important ecosystem process which is largely provided by insects. Insect as food for animals and human being. The knowledge regarding immature stages of insect-pests and understand site of oviposition, site of pupation and larval behaviour can allow for timely and effective management, thus we can reduction in the qualitative and quantitative losses of yield and increase the profit.
Role of Synergists in Resistance ManagementJayantyadav94
Any chemical which in itself is not toxic to insects as dosages used, but when combined with an insecticide greatly enhances the toxicity of insecticide is known as synergist. Process of activation is synergism. Helps in penetration and stabilization of insecticides, and prevents the detoxification of insecticides
Here I would like to inform you in host selection process by the parasitiods.I hope It would increase your understanding on the steps involved n the host selection process.............................
Rapid phenotyping of prawn biochemical attributes using hyperspectral imagingStuart Hinchliff
A seminar I presented on research concerning the possibility of using hyperspectral imaging to non-invasively phenotype prawns. This phase of the project involved the development of software remove the background, identify individual prawns and their spectra, apply machine learning and statistical algorithms to the results, and develop an intuitive GUI.
Rapid phenotyping of prawn biochemical attributes using hyperspectral imagingStuart Hinchliff
A seminar I presented on using hyperspectral imaging to non-invasively phenotype prawns. The project focused on removing the background of the images, developing an intuitive UI and applying statistical algorithms. The aim was to train a model to predict the biochemical attributes of prawns using their spectra.
Importance of study of immature stages of insects in agricultureSanju Thorat
The type of life cycle will vary with the insect-pest. However, most pests have certain weak points during their life cycle when they are the most vulnerable to manage. Some insect are predators, either as larvae or in both larval and adult stages. The decomposition of organic waste, such as dung and manures are an important ecosystem process which is largely provided by insects. Insect as food for animals and human being. The knowledge regarding immature stages of insect-pests and understand site of oviposition, site of pupation and larval behaviour can allow for timely and effective management, thus we can reduction in the qualitative and quantitative losses of yield and increase the profit.
Role of Synergists in Resistance ManagementJayantyadav94
Any chemical which in itself is not toxic to insects as dosages used, but when combined with an insecticide greatly enhances the toxicity of insecticide is known as synergist. Process of activation is synergism. Helps in penetration and stabilization of insecticides, and prevents the detoxification of insecticides
Here I would like to inform you in host selection process by the parasitiods.I hope It would increase your understanding on the steps involved n the host selection process.............................
Rapid phenotyping of prawn biochemical attributes using hyperspectral imagingStuart Hinchliff
A seminar I presented on research concerning the possibility of using hyperspectral imaging to non-invasively phenotype prawns. This phase of the project involved the development of software remove the background, identify individual prawns and their spectra, apply machine learning and statistical algorithms to the results, and develop an intuitive GUI.
Rapid phenotyping of prawn biochemical attributes using hyperspectral imagingStuart Hinchliff
A seminar I presented on using hyperspectral imaging to non-invasively phenotype prawns. The project focused on removing the background of the images, developing an intuitive UI and applying statistical algorithms. The aim was to train a model to predict the biochemical attributes of prawns using their spectra.
Multi-object tracking is a computer vision task which can track objects belonging to different categories, such as cars, pedestrians and animals by analyzing the videos.
Introduction to image processing and pattern recognitionSaibee Alam
this power point presentation provides a brief introduction to image processing and pattern recognition and its related research papers including conclusion
ABSTRACT Feature extraction plays a vital role in the analysis and interpretation of remotely sensed data. The two important components of Feature extraction are Image enhancement and information extraction. Image enhancement techniques help in improving the visibility of any portion or feature of the image. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image. This presented work focuses on the various feature extraction techniques and area of optical character recognition is a particularly important in Image processing. Keywords— Image character recognition, Methods for Feature Extraction, Basic Gabor Filter, IDA, and PCA.
IMAGE SUBSET SELECTION USING GABOR FILTERS AND NEURAL NETWORKSijma
An automatic method for the selection of subsets of images, both modern and historic, out of a set of
landmark large images collected from the Internet is presented in this paper. This selection depends on the
extraction of dominant features using Gabor filtering. Features are selected carefully from a preliminary
image set and fed into a neural network as a training data. The method collects a large set of raw landmark
images containing modern and historic landmark images and non-landmark images. The method then
processes these images to classify them as landmark and non-landmark images. The classification
performance highly depends on the number of candidate features of the landmark.
I MAGE S UBSET S ELECTION U SING G ABOR F ILTERS A ND N EURAL N ETWORKSijma
An automatic method for the selection of subsets of
images, both modern and historic, out of a set of
landmark large images collected from the Internet i
s presented in this paper. This selection depends o
n the
extraction of dominant features using Gabor filteri
ng. Features are selected carefully from a prelimin
ary
image set and fed into a neural network as a traini
ng data. The method collects a large set of raw lan
dmark
images containing modern and historic landmark imag
es and non-landmark images. The method then
processes these images to classify them as landmark
and non-landmark images. The classification
performance highly depends on the number of candida
te features of the landmark.
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
2. Powerpoint Templates
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OUTLINE
• Introduction
• Image-based recognition
• Colour histogram and GLCM
• Pattern recognition
• Extension theory
• Hybrid approach
• Stacked spatial-pyramid kernel
• Ontology-based insect recognition
• KNN-Spectral Regression LDA
• Histogram of local appearances features
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Introduction
• Automation of insect identification is required because
there is a shortage of skilled entomologists.
• Recently, image-based insect recognition has emerged
as a new field of study concerning image processing
and intelligent pattern recognition replacing inefficient
traditional technique.
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CHARACTERISTICS OF INSECTS
• Most of the insects are composed of several sub parts
which are antennae, tails, wings.
• These parts are extracted in image processing method
to be used in recognition of insects as well as other
important features such as color and shape.
• Majority of the researchers use wing as the most
crucial part for feature extraction in classification of an
insect.
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1. Image-based recognition
• The general principles of classification include image acquisition and image
processing.
• Image acquired by selecting a number of different species of insects as the
specimen of the research.
• A picture of each species selected is taken using any camera to obtain an
image.
• The image in color scale will be converted to grayscale then converted ti
binary image where only white and black color is produced.
The region of interest (ROI) will be separated from the background
image.
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Image-based recognition
• The next step is image processing.
• The image will undergo feature extraction, and the features
extracted will undergo classification process.
• At this stage, different method will produce different output.
• There are several methods used for insect recognition such as
fuzzy classifier, nearest neighbor classifier and also artificial
neural network (ANN)
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2. Colour histogram and GLCM
• Image preprocessing algorithm is used to segment out the
region of interest (ROI) from the insect image.
• Then, color features which are represented by color
histogram are extracted from ROI that can be used for
coarse color matching.
• The matching is done by comparing the correlation of the
feature vectors with certain threshold .
• The image samples that passed this stage will undergo
the next stage that is fine level matching.
• The fine level features are represented by coefficients
such as energy, entropy and correlation of GLCM of
preprocessed image blocks.
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Colour histogram and GLCM
• When doing research one hundred species of insects are
selected as samples.
• All the image samples are captured from the right wing of
the insects.
• A part of them are forewing with backwing, the rest are
forewing.
• The image is resized to speed up the processing speed
and then filtered using mean shift algorithm.
• Then, it is converted to gray scale image and binarized
with certain threshold.
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Colour histogram and GLCM
• To reduce the effect of rotation and translation during
image acquisition, a universal coordinate must be located
by connecting the centroid of the foreground region and
rotate the image around the centroid until the connected
line is horizontal.
• Least-square method is applied to fit the upedge and
downedge of the insect wing with straight lines.
• ROI is determined afterwards and the image is aligned
using Gaussian before feature extraction.
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Colour histogram and GLCM
• The color image is transformed from RGB (Red-Green-
Blue) space into HSV (Hue-Saturation-Value) space
before construct the histogram.
• To minimize the effect of illumination variation, only hue
and saturation is take into consideration.
• The histogram for hue and saturation component is
calculated and it shows that the histogram for same
species is distributed similarly.
• As for GLCM, it estimates image properties related to
second order statistics for example energy, entropy,
correlation and homogeneity.
• This technique can identify insects from low resolution
wing images and the efficiency and accuracy is proven
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3.Pattern recognition
• Pattern recognition can be specifically defined as “the act of taking in raw
data and taking an action based on the category of the pattern.
• They further explained that the process involve three stages which are,
Input data and information collected from the sample using sensor,
Feature extraction mechanism that compute numeric or symbolic data
from the sample-colour, texture and edges.
Classification scheme that determine the group of the sample.
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4. Extension theory
• Extension theory is a method that constructed the matter
element matrix of the insect based on mean and variance
of the image features.
• Matter-element matrix to be recognized includes the
object itself, the feature and the feature value of the object
in this case is an insect.
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Extension theory
• Feature extraction of insects.
• Image is captured in real time and converted to gray
scale image.
• Segmented by the adaptive method so that the binary
insect image is separated from the grain and the
background.
• Seventeen morphological features are extracted from
the binary image and then are normalized.
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Extension theory
• Matter element of features of the insects are then
constructed.
• The system is trained to identify the species of the insects
from the given samples.
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5. Stacked spatial-pyramid kernel
• Stacked Spatial-Pyramid Kernel method boost up the
performance of standard stacking method hence
provides greater accuracy for the sample classification.
• Stacking is one of digital image processing technique
that combines multiple classifiers with each classifiers
to manage their own feature space
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Stacked spatial-pyramid kernel
• Conventional stacking method had disadvantage of
losing spatial information belonged to the features
since only the raw combination scores are being
submitted as input to the final classifiers.
• New stacking structure that has the capability to retain
the spatial information as well as increase the
classification result accuracy by quantizing input data.
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Stacked spatial-pyramid kernel
• First, the HOG(histogram of oriented gradient) and
SIFT(scale invariant feature transform) analysis are
constructed from the edge and dense grid of patches
provided by the image samples while beam angle
histogram taking care of sample’s shape.
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6.Ontology-based insect recognition
• Ontology based image retrieval has gained much
popularity in semantic image retrieval in recent years.
• Ontology based recognition system consist of a layer
that hold four types of system identification namely,
visual feature ontology, image feature ontology,
classification module, and Insect Morphology and
Taxonomy Ontology(IMTO)
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Ontology-based insect recognition
• This layer will give support and assist the insect image
recognition and annotation.
• Visual feature extract the image while classification
module pair the image with the insect image previously
stored in feature knowledge database.
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7. KNN-Spectral Regression LDA
• KNN-Spectral regression
LDA is one of the best
method that can be
applied in automatic
insect recognition.
• This method is based on
the face recognition
principle.
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KNN-SpectralRegression LDA
• The function of LDA algorithm is to spread the
distribution of different classes samples wider into
space while the sample that belong to the same feature
is grouped more closely to each other.
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8.Histogram of local appearances features
• This is general-purpose computer vision methods, and
associated mechanical hardware, for rapid-throughput
image capture, classification, and sorting of small
arthropod specimens.
• This method is used in classifying insects which are
hard to differentiate visually.
• The bag-of-feature approach which extracts a bag of
region-based features from the image without take into
account their relative spatial arrangement is used here.
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Histogram of local appearances features
• This approach involves 5 stages namely.
Region detection
Region description
Region classification
Combination of detected features into a feature vector
Final classification of the feature vector
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Histogram of local appearances features
Region detection
• Types of detectors used
include Hessianaffine
detector, Kadir entropy
detector and principle
curvature-based region
detector (PCBR).
Region description
• The detected region is
then described using
SIFT representation.
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Histogram of local appearances features
Region classification
• In classification into
features, a histogram is
assigned to each feature
formed consisting of the
number of SIFT vectors
Combination of detected
features into a feature
vector
• Feature vector is created for each
of the three region detectors using
• GMM (Gaussian Mixture Model)
concatenated feature histogram
(CFH) method that allows the use
of general classifier from the
machine learning literature.
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Histogram of local appearances features
Final classification of the feature vector
The classification of the specimens is done by an
assembler of logistic model tress.
This stage is to train a classifier to assign the correct
labels to the bag of keywords where this keywords is
obtained from the clustering of SIFT vectors.
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9. Hybrid approach
• Hybrid approach of classifying insects uses a method
called discriminative local soft coding (DLsoft).
• Soft coding strategies, a feature vector of the insect
image is encoded by a kernel function of distance.
• As for the discriminative part, Multiple Kernel Learning
(MKL) is used for the classification to improve the
drawbacks of the soft coding alone.
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Hybrid approach
• The dataset is divided into two.
Training set & Testing set
• First, all the images are transformed into gray scale.
• SIFT features of patches are extracted by densely
sampled from each image.
• SIFT is done by dividing the images into several
section of squares and each section will compute its
own histogram.
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Hybrid approach
Subsequently, max pooling over all the vectors of
patches in the same image is calculated.
The final presentation of an image sample is spatial
pyramid pooled vector which can be used by any
machine learning method for classification.