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Pest Classification Essay

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Buy custom The Potential Hazards to Human of Pest Control Chemicals essay

The Potential Hazards to Human of Pest Control Chemicals Buy custom The Potential Hazards to Human of Pest Control Chemicals essay

The term pest is used to denote anything that threatens the environment or human health. Pests can be weeds or ants, mice or even deer being harmful for agriculture. Pesticides are used to kill and repel pest, avert animal diseases. Thus, they are very dangerous and the risks of using them in domestic situations or agriculture should be considered beforehand. Pest control chemicals may be pesticides, fungicides, insecticides, weedicides, rodenticides, repellents or biocides.

In case prevention is not effective, people have to use pest control chemicals. If pest control chemicals are used in domestic conditions, it is necessary to follow the instructions on the product label. Moreover, it is better to hire a professional who knows how to deal with the pests. Despite the toxicity of pest control chemicals, they may be harmful to humans and the environment in case they are used inappropriately or carelessly.

There are many potential hazards of pest control chemicals to humans. First of all, fungicides can irritate eyes, skin and throat when inhaling its spray or dust. Secondly, herbicides are irritating for human skin, nasal passages and chest. Moreover, if a person inhales its dust, she or he may feel dizzy. Thirdly, the potential hazard of insecticides is poisoning which may lead to a malfunction of a nervous system. Lastly, the use of pest control chemicals may cause Alzheimer’s, Parkinson's diseases or any kind of cancer. However, the hazard to humans depends on the toxicity of the pest control chemical and the amount of exposure. So, it is better to use products which are toxically low and consider the level of exposure.

To sum up, it is important to be aware of the hazards of the potential pest control chemicals before using them. People should use them properly in order to not harm themselves and the environment. It is necessary to read the product label and ask for a piece of advice from sales staff before buying a pest control chemical. Furthermore, it is safer to prepare only the smallest portion of pest control chemicals required for killing or controlling a specific pest. Finally, special equipment is needed to work with these chemicals, which has to be thoroughly washed after usage.

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InsectTech and Pest Control Ltd essay topics, buy custom InsectTech and Pest Control Ltd essay paper sample cheap, service

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It is very important for the two companies InsectTech and Pest Control Ltd. To formulate a strategic negotiation which supports the distribution agreement founded on the principle of equality between the two companies. The relationship between the two companies as distributor and supplier has to be charted based on partnership which is fair and egalitarian in approach. It is necessary that the terms, conditions and clauses are not tricky and devised on cunning cut from the bargain. The negotiation between InsectTech and Pest Control, should be simple, clear, balanced and fair. The terms should be clearly stated and any disagreement should be open to healthy and amicable promise of cooperation between the two companies. There should be effective steps taken to avoid any legal conflict between the two parties. This necessitates integration and understanding which can nurture long lasting partnership between InsecTech and Pest Control.

The unique product with sophisticated yet simple design which is specialized in technical expertise using high energy electricity and vibration control to keep the mosquitoes and insects away is remarkable. The consideration taken into account that these vibrations do cause any health hazard to human being is also commendable. The business plan which has taken effective steps to engage in cross boundary business and distributorship agreement is comprehensive. The trendy name" the mozzie shaker' is charming and effective.

The negotiation strategy should engage the key people at InsectTech and Pest control with team of lawyers, managing directors, and the R&D people helping evolve a sound draft of distribution Agreement based on fair negotiation parameters.

The negotiation strategy should be based on partnership which allows for an agreement which is based on detailed consideration of the terms, expiration criteria, balanced revenue, effective advertising and protection of the intellectual property rights. The case study should be determined based on the pricing and the product specifications. The litigation aspect of the partnership must be taken into account. The structural details should also be integrated into the business framework. The territorial specifications need to be addressed taking into account the competitors, with emphasis on quota limit and articulate agreement with regard to termination. The technical expertise should be promoted and encapsulated in the distribution draft or agreement. Consideration should be made to identify the dealers and remarketers within the specified territory. A formal Distribution Agreement should be drafted in the light of the unique product targeting a niche market. The clauses should be specified and terms and conditions clearly stated with right expectations and targets. The role of competition law along with business law should be integrated into the framework.

The Distributors Agreement provides the partners in business relationship with and agreement which is tailored to serve the interest of both the parties in fair and compatible manner within the legal framework. The interest and gains of both the parities is secured through specific terms and conditions. It can serve as the head start for the future professional relationship of the interacting companies. It is platform which gets the deal moving in the right direction with a sense of commitment. The agreement should be drafted clearly and professionally under legal guidance, even thoguth it is not a legal advice. The requirements should be specified and reviewed under approval of a certified attorney. The draft should then be considered an effective documentation for the business relationship which will thrive under the contract. The distribution rights which specify the exclusive right of the distributor to sell to customers is identified and initiated. The specifications of the geographic territory are also indicated. The details of the company offices along with pricing are included in the contract. The inclusion of transportation costs is also taken into consideration. The taxation and invoice obligations are fully authorized under valid tax collecting authority. InsectTech should clearly define its legal outline based on the diverse criteria as discussed below as central to the negotiation initiative with Pest Control.

Terms: The terms are defined under specific information pertaining to the commencement and expiration or termination of the agreement. It is based on mutual consent which describes the nature of distributorship. It is renewable. The specifics about date, month and time is precisely indicated. The particular provisions of the agreement are detailed with precision. It also specifies the mode of payment which can be cash or credit. The Company has the specific right to permit or revoke any credit dealings with any customers or partners. The invoices and scheduled deliveries are specified in terms of mode of payment. According to Cueto (2010) terms must be specified within the clauses which are set for "the duration of the agreement and is automatically renewable"(Cueto, 2010). There is provision for re-negotiation after the duration expires. This period is determined by the rules of the business and the industry.

"Cleverly crafted words and phrases in a distributor agreement rarely extend the life of a partnership between a distributor and a manufacturer. A partnership lives only so long as both partners believe that there is a benefit to a continuing relationship. Once perceived value erodes, the partnership is finished, followed closely by the expiration of the agreement."(Blazer, 2003).The signatories need to understand the clauses within the framework of the distribution agreement before it is launched. This will provide an optimistic partnership which will determine long term relationship ion business partnership. It will also save the company from any legal dispute or early termination. The financial resource allocation should also be skillfully handled. Blazer (2010) further observes that "unbalanced agreements more frequently result in a legal scuffle, striving to craft a well-balanced distributor agreement is worth the effort. An ounce of preventive energy striving to draft a balanced agreement is worth a pound of legal energy struggling to avoid a costly award of damages in either court or arbitration."(Blazer, 2010).

Products: The product needs to be developed confidently and realistically. In this case the company has designed a product which consists of a small box which gives off ultrasonic vibrations. It is powered by electricity. The vibrations have been found to be highly effective in keeping mosquitoes and other insects away and they work over an area of 500 square meters. Their tests show that the vibrations cause no damage to human health. InsectTec believes that the product would be highly successful in the Anyland market. It does not yet have a marketing name - the R & D (research and development) people at Pest Control Ltd refer it as "the mozzie shaker". InsectTec respects Pest Controls intellectual property rights in their methods and know-how in respect of the product. However, they want the right to market the product under a name suitable to the Anyland market. InsectTec also want complete freedom as to the method of marketing. Cueto(2010) further affirms that it is important to "specifically describe and identify the product developed or owned by a company along with all options to the products; all future versions of the products; and all enhancements, revisions, or modifications made to the products by company."(Cueto, 2010).

Territory: "Be sure to always indicate the specific geographic areas where the product(s) will be distributed. Also be sure to include terms of exclusivity to keep your channel clear of competitors."(Cueto, 2010).

End-User:The role of the end user is significant; one needs to identify all people or entities that will obtain the product(s). The role of the distributor is significant in providing precise instruction to the customer with regard to the use and implementation and health hazards related to the product.

Intellectual Property Rights: It is very important to identify the subtle legal rights which specifically address the " idea, concept, technique, invention, discovery or improvement"(Cueto, 2010). It is also applicable to the authorship and future posts pertaining the product.

Continuation or survival clauses: The clauses pertaining termination should also be clearly mentioned so that the distributor is in agreement of the future prospects of the company in partnership.

Title to equipment: The Company should structure the reserves in such a manner that the securities related to per unit sale of the product are precisely defined by the agreement. It should meet the interest of all parties and be executed under the financial statement which is verifiable by the selected instruments which measures the securities of the company. This ensures that the title to equipment is transferred to the Distributor form the Company under full payment contract defined per unit of sale.

Competitive Equipment: There is an implied restriction imposed on the Distributor in terms of selling the product ore representing the knowledge to any of the competitors unless approved by the Company.

Duration of Agreement: The duration of the distribution agreement will last as long as expiration or termination is not implemented. It requires careful articulation of the deadline. The more detailed and outlined it is. better it is for both the parties.

Marketing and Support

It is very important to identify the structural support for marketing which will significantly impact the sales and the distribution of the equipment in the process of implementation of the distribution agreement.

1. Sales. It is important that distributor aggress to use the best technique to promote sales and the distribution process which ensures:

a. making sure that it has well established and appropriate premises which re attractive, accessible and fully equipped.;

b. There is adequate provision of well trained staff and technical support for the equipment;

c. The promotional campaigns are used effectively to effectively campaign the product;

d. The company provides with product information and forecasts which introduce any modification or changes in the equipment. The forms need to be signed by the distributor and the Company. In this case InsectTech and the Pest Control ltd.

2. Advertising. The role of advertising is very powerful and should be encouraged by the distributor to promote campaigns and advertising for sales and promotion. It should be professionally manage and controlled. The documentation should be conducted according to the nature of the product in harmony with the specifications of the equipment. The technical and structural information should be managed and implemented with professionalism. There will be provision for assistance for implementation purposes.

3. Training. The Company is responsible for training and employment of training representatives at various sites and locations based on the territorial and geographic factors. There should be encouragement for enrollment of sufficiently qualified personnel who should be salaried employees. The expense should be managed within the framework of the distribution agreement.

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Pest Analysis Essays Writing UK

PEST Analysis Essay UK Essay Writing, MBA Dissertation Writing

Pest analysis is a scan of the external macro environment in which the firm operates. It includes the political, economic, social and technological factors that impact the firm’s growth and advancement.

Political Factors:

These include the ones that related to governmental regulations, rules and legal issues. Some examples include tax policy, tariffs, trade restrictions and political stability etc.

Economic Factors:

Factors such as interest rates, economic growth, exchange rates and inflation etc. are a part of this. These are the factors that influence the purchasing power of customers and the firms cost of capital.

Social Factors:

These include the demographic and cultural aspects of the environment the company operates in. These affect customer needs and preferences and size of potential markets. Social factors include health consciousness, population growth, age distribution, trends, customs etc.

Technological Factors:

These include research and development activities, automation, technology incentives etc. These factors can lower barriers to entry, reduce minimum efficient production levels etc. (Worthington, pg 527)

REFERENCES

Ian Worthington. (2006), The Business Environment, Financial Times/Prentice Hall, page 500-527.

Agricultural Pest Detection and Classification - Essay UK Free Essay Database

Free Environmental Studies essays Agricultural Pest Detection and Classification

In agriculture research of automatic pest detection
and disease detection is essential research topic as it may prove
benefits in monitoring large fields of Cotton crops and thus
automatically detect symptoms of disease as soon as they appear
on Cotton plant. The studies of plant disease refer to the studies
of visually observable patterns of a particular plant. Nowadays
crops faces many diseases such as damage of the insect/pest is
one of the major disease. A common practice for Cotton plant
scientists is to estimate the damage of plant (leaf, stem) because
of pest by an eye on a scale based on percentage of affected area.
It results in subjectivity and low throughput. This paper provides
a advances in various methods used to study plant diseases/traits
because of pest using image processing. The methods studied are
for increasing throughput and reducing subjectiveness arising
from human experts in detection of plant diseases.

I. INTRODUCTION
Computer vision techniques have great significance on the
automatic identification of the images of Cotton insect pests.
Those techniques not only can decrease the labour, but also
can improve the speed and precision of the identification
and diagnosis, when compared to manual method. In this
regard, recognition of paddy field insect pests is challenging
because the insect pests are highly articulated, they exhibit
a high degree of intra-pest variation in size and colour, and
some insect pests are difficult to distinguish visually, despite
prominent dorsal patterning. The manual classification of such
insect pests in paddy fields can be time consuming and requires
substantial technical expertise. The task becomes more
challenging when insect pests are to be recognised from still
images using an automated system. Images of one insect pest
may be taken from different viewpoints, cluttered background,
or may suffer transformation such as rotation, noise, etc. So
it is likely that two images of the same insect pest will be
different. To address these challenges, we have adopted the
gradient-based features in classifying images of Cotton crops
insect pests. The primary advantage of this approach is that it
is invariant to changes in pose and scale as long as the features
can be reliably detected. Furthermore, with an appropriate
choice of classifier, not all features need to be detected in
order to achieve high classification accuracy. Hence, even if
some features are occluded or fail to be detected, the method
can still succeed.
In this paper various techniques to identify the diseases and
pests using infected images of various symptoms and leaf spots
and pests on crops were studied. Images are captured by digital
camera mobile and processed using image growing, then the
Figure 1: Selected species of cotton insect pests.
part of the leaf sport has been used for the classification
purpose of the train and test. The technique evolved into
the system is both Image processing techniques and advance
computing techniques. Image analysis can be applied for the
following purposes:
1) To detect diseased leaf, stem, fruit and pests on Cotton
crops.
2) To detect quantity of affected area by disease and pests.
3) To find the boundaries of the affected area.
4) To determine the color of the affected area.
5) To determine size and shape of leaf and pests.
6) To identify the Object correctly.
The rest of this paper is organized as Section II provides
short review of agricultural pest detection and classification
techniques. Section III provides short summery of revived
classification techniques. In end of paper we conclude about
revived techniques.
II. REVIEW OF AGRICULTURAL PEST DETECTION AND
CLASSIFICATION TECHNIQUES
In this section different pest detection techniques implemented
by different researchers from all over the world are
summarised. P. Revathi et. al[7] detected Cotton leaf spot
diseases in [7] by using Homogenous Segmentation based
Edge Detection Techniques. This system is analyzed with
eight types of cotton leaf diseases they are Fusarium wilt,
Verticillium wilt,Root rot,Boll rot,Grey mildew ,Leaf blight
,Bacterial blight,Leaf curl.In these work symptoms of cotton
leaf spot images are captured by mobile and classification is
done by using neural network. In this work a homogeneity
operator can take the difference of the center pixel and a
pixel that is two or three pixels away. The main aim Research
2
work is to use Homogeneity-based edge detector segmentation,
which takes the result of any edge detector and divides it by the
average value of the area. This work has been implemented in
the real time software and produces best results. The software
is very fast and time intense, low cost, automatically identify
the diseases and pest recommendation to farmers through a
mobile phone.
Ajay A. et. al [5] presented Eigen feature regularization
and extraction technique by this detection of three diseases
can be done. This system is having more accuracy, than that
of the other feature detection techniques. With this method
about 90% of detection of Red spot i.e. fungal disease
on cotton leaves is detected. Dheeb Al Bashish and et. al
2010[6] proposed image processing based work is consists
of the following main steps, In the first step the acquired
images are segmented using the K-means techniques and then
secondly the segmented images are passed through a pretrained
neural network .The images of leaves taken from Al-
Ghor area in Jordan. Five diseases that are prevalent in cotton
leaves were selected for this research; they are: Early scorch,
Cottony mold, Ashen mold, late scorch, tiny whiteness. The
experimental result indicates that the neural network classifier
that is based on statistical classification support accurate and
automatic detection of leaf diseases with a precision of around
93%.
R. G. Mundada et. al[24] have proposed a system to
detect white flies, aphids and trips on the infected crops in
greenhouse. Images of the infected cotton leaf are captured by
a camera and pre-processed by converting these images from
RGB to gray scales and filtering in order to obtain an enhanced
image set of pests. properties such as region properties and
gray covariance matrix properties such as entropy, mean,
standard deviation, contrast, energy, correlation and eccentricity
were extracted from these images. The classification was
performed by the use of support vector machines. proposed
system is used for rapid detection of pests and exhibits the
same performance level as a classical manual approach. R.
K. Samanta et. al[25] presented system for tea insect pests
classification using correlation-based feature selection (CFS)
and incremental back propagation learning network (IBPLN).
The authors have created a database concentrating on eight
major insect pests from the records of different tea gardens
of North-Bengal districts of India. The database consists of
609 instances belonging to eight classes described by 11
attributes (signs and symptoms); all of which are nominal. The
classification was performed using artificial neural networks.
The classification results were compared with the original
feature set and reduced feature set. Their study demonstrates
that CFS can be used for reducing the feature vector and
CFS+IBPLN combination can be used for other classification
problems.
T. Jaware et. al[26] presented a Fast and accurate method
for detection and classification of plant diseases. The proposed
algorithm is tested on main five diseases on the plants; they
are: Early Scorch, Cottony mold, Ashen Mold, Late scorch,
tiny whiteness. Initially the RGB image is acquired then
a color transformation structure for the acquired RGB leaf
image is created. After that color values in RGB converted
to the space specified in the color transformation structure.
In the next step, the segmentation is done by using K-means
clustering technique. After that the mostly green pixels are
masked. Further the pixels with zero green, red and blue values
and the pixels on the boundaries of the infected object were
completely removed. Then the infected cluster was converted
into HIS format from RGB format. In the next step, for each
pixel map of the image for only HIS images the SGDM
matrices were generated. Finally the extracted feature was
recognized through a pre-trained neural network. The results
show that the proposed system can successfully detect and
classify the diseases with a precision between 83% and 94%.
Y. Tian et. al[27] presented a method to monitor four main
wheat plant diseases: Powdery Mildew, leaf rust Puccinia
triticina, leaf blight, Puccinia striiformis and three features
obtained are color feature, texture feature, and shape feature
which further used as training sets for three corresponding
classifiers. This system is mainly classified into three main
steps: data acquisition, feature extraction, and classifier design.
Multiple Classifier System (MCS) includes number of
classifiers which can provide higher classification accuracy.
T. Rumpf et. al[28] presented a system for the detection
and differentiation of sugar beet diseases based on Support
Vector Machines and spectral vegetation indices. They used
Cercospora leaf spot, leaf rust and powdery mildew diseased
leaves as study samples. The main aim was to identify these
diseases before their symptoms became visible. In this proposed
work nine spectral vegetation were used as features for
an automatic classification. The experimental result indicates
that the discrimination between healthy sugar beet leaves and
diseased leaves classification accuracy up to 97%.
S. Phadikar et. al[29] presented an automated classification
system based on the morphological changes caused by brown
spot and the leaf blast diseases of rice plant. To classify the
diseases Radial distribution of the hue from the centre to the
boundary of the spot images has been used as feature by
using Bayes and SVM Classifier. The feature extraction for
classification of rice leaf diseases is processed in the following
steps: firstly images acquired of diseased rice leaves from
fields. Secondly preprocessing the images to remove noise
from the damaged leaf and then enhanced the quality of
image by using the [mean filtering technique. Thirdly Otsus
segmentation algorithm was applied to extract the infected
portion of the image, and then radial hue distribution vectors
of the segmented regions computed which are used as feature
vectors. Here classification performed in two different phases
.In first phase uninfected and the diseased leaves are classified
based on the number of peaks in the Histogram.In the second
phase the leaf diseases are classified by Bayes classifier.
This system gives 68.1% and 79.5% accuracies for SVM and
Bayesclassifier based system respectively.
S. S. Sannakki et. al[30] in paper titled A Hybrid Intelligent
System for Automated Pomegranate Disease Detection and
Grading proposed a system not only identifies various diseases
of pomegranate plant but also determines the stage in which
the disease is. The methodology is divided into four steps:
1) The images acquisition where the images were captured
by using digital camera.
3
2) The image preprocessing creates enhanced image that is
more useful for human observer. Image preprocessing
uses number of techniques like image resize, filtering,
segmentation, morphological operations etc.
3) Once the image has been enhanced and segmented in
image postprocessing noises like stabs, empty holes
etc. are removed by applying morphological operations,
region filling. Further the features are extracted like color
. shape ,texture.
4) Once the features are extracted to which disease class the
query image in belongs different machine learning techniques
are used like Artificial neural networks, Decision
tree learning, genetic algorithms, Clustering, Bayesian
networks, Support Vector Machines, Fuzzy Logic etc.
P. Keskar et. al[31] presented a leaf disease detection and
diagnosis system for inspection of affected leaves and identifying
the type of disease. This system is comprised of four
stages: To improve the appearance of acquired images image
enhancement techniques are applied. The enhancement is done
in three steps: Transformation of HSI to color space in first
stage .In the next stage analyzing the histogram of intensity
channel to get the threshold. Finally intensity adjustment by
applying the threshold. The second stage is segmentation
which includes adaption of fuzzy feature algorithm parameter
to fit the application in concern. The feature ex traction stage is
comprised of two steps spot isolation and spot extraction. For
identification of spot identification algorithm is used is called
component labeling. In feature extraction phase three features
are extracted namely color, size and shape of the spots. In
fourth stage classification is performed by Artificial Neural
Network.
A. Meunkaewjinda et. al[32] presented diagnosis system
for grape leaf diseases is proposed. The proposed system is
composed of three main parts: Firstly grape leaf color extraction
from complex background, secondly grape leaf disease
color extraction and finally grape leaf disease classification.
In this analysis back-propagation neural network with a selforganizing
feature map together is utilize to recognize colors
of grape leaf. Further MSOFM and GA deployed for grape
leaf disease segmentation and SVM for classification. Finally
filtration of resulting segmented image is done by Gabor
Wavelet and then SVM is again applied to classify the types
of grape leaf diseases. This system can classify the grape leaf
diseases into three classes: Scab disease, rust disease and no
disease. Even though there are some limitations of extracting
ambiguous color pixels from the background of the image.
The system demonstrates very promising performance for any
agricultural product analysis.
L. Liu et. al[33] a system for classifying the healthy and
diseased part of rice leaves using BP neural network as
classifier. In this study rice brown spot was select as a research
object. The images of rice leaves were acquired from the
northern part of Ningxia Hui autonomous region. Here the
color features of diseases and healthy region were served as
input values to BP neural network. The result shows that this
method is also suitable to identify the other diseases.
III. SUMMERY ON CLASSIFICATION TECHNIQUES
This section will discuss some of the popular classification
techniques with their advantages and disadvantages that are
used for plant leaf and pests classification. In plant pests
and leaf classification leaf is classified based on its different
morphological features. Some of the classification techniques
used are Neural Network, Genetic Algorithm, Support Vector
Machine and Principal Component Analysis, k-Nearest
Neighbor Classifier. Plant leaf disease classification has wide
application in Agriculture.
1) k-Nearest Neighbor: The main disadvantage of the KNN
algorithm is that it is a slow learner, i.e. it does not learn
anything from the training data and simply make use the
training data itself for classification. Another disadvantage
is this method is also rather slow if there are a large
number of training examples as the algorithm must have
to compute the distance and sort all the training data at
each prediction. Also it is not robust to noisy data in case
of large number of training examples. The most serious
disadvantage of nearest neighbor methods is that they are
very sensitive to the presence of irrelevant parameters.
2) Support Vector Machine: Main advantages of SVM
are Its prediction accuracy is high, Its working is robust
when training examples contain errors, Its simple
geometric interpretation and a sparse solution and
Like neural networks the computational complexity of
SVMs does not depend on the dimensionality of the
input space. Drawbacks are this classifier involves long
training time, In SVM it is difficult to understand the
learned function (weights) and the large number of
support vectors used from the training set to perform
classification task.
3) Artificial Neural Network (ANN): it is simplest single
layer networks whose weights and biases could be
trained to produce a correct target vector when presented
with the corresponding input vector and it can solve only
linear problems
4) Probabilistic Neural Networks: The main disadvantage
of PNN is it requires large storage space but PNNs
are much faster than multilayer perceptron networks,
PNNs are used in on-line applications where a real-time
classifier is required
5) Fuzzy Logic: As Fuzzy logic classifiers has very high
speed they are preferable in cases where there is limited
precision in the data values or when classification is
required in real time and Drawback of Fuzzy logic
as classifier is dimensionality because of this classifier
is inadequate for problems having a large number of
features. Also it gives poor performance while there is
a limited amount of knowledge that the designer can
incorporate in the system.
IV. CONCLUSION
This paper provides the survey of different techniques for
pests and leaf disease detection. Main aim of this work is
to study various pest detection techniques, characteristics of
pest and various diseases on cotton crops with the help of
4
image processing techniques likes image segmentation, feature
extraction, classification, with the help of these techniques
we will be identifying various agricultural pests on various
crops or specially cotton crops. which helps the farmer to
take correct action to increase production. For the detection of
pests speed and accuracy is important factor to be considered.
Hence there is working on development of automatic, efficient,
fast and accurate system which is use for detection pests
and disease on unhealthy cotton leaf. speed and accuracy are
the main factors keeping in mind and hence Work can be
extended for development of hybrid algorithms with neural
networks in order to increase the recognition rate of final
classification process. In future we can development of real
time implementation of this algorithm in farm for continuous
monitoring and detection of plant diseases. In real time system,
we can monitor and give exact solution to avoid various
diseases on cotton plant.
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