• Corpus ID: 5645500

Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method

@article{Revathi2014CottonLS,
  title={Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method},
  author={P. B. Revathi and M. Hemalatha},
  journal={International Journal of Scientific Engineering and Technology},
  year={2014},
  volume={3},
  pages={22-30}
}
  • P. Revathi, M. Hemalatha
  • Published 2014
  • Computer Science
  • International Journal of Scientific Engineering and Technology
This research work exposes the novel approach of analysis at existing works based on machine vision system for the identification of the visual symptoms of Cotton crop diseases, from RGB images. Diseases regions of cotton crops are revealed in digital pictures, Which were amended and segmented. In this work Proposed Enhanced PSO feature selection method adopts Skew divergence method and user features like Edge, Color, Texture variances to extract the features. Set of features was extracted from… 

Figures and Tables from this paper

A Survey Disease Detection Mechanism for Cotton Leaf: Training & Precaution Based Approach
TLDR
A methodology for detecting cotton leaf disease early, using image processing techniques and artificial neural network (ANN), and work with the current and future precaution for the cotton tree to protects it from future disease & maintain it to improve its good production as well as life.
Image Processing Based Approach for Diseases Detection and Diagnosis on Cotton Plant Leaf
TLDR
The proposed system is based on image processing, the infected cotton plant leaf image is first segmented using the K-means algorithm, then Color and texture features have been extracted from the segmented image and disease detection through feature classification will be done by support vector machine.
A Review of Grape Plant Disease Detection
TLDR
Various image processing and classification techniques to detect and further eliminate plant diseases which has tremendous significance on the productivity of agriculture are reviewed.
Paper on Identification of Plant Diseases Using Image Processing Technique
TLDR
An overview of different classification techniques used for plant leaf disease classification using machine learning for detection of various cotton crop diseases and to classify them is provided.
Random forest based classification of diseases in grapes from images captured in uncontrolled environments
TLDR
This work proposes a system for classifying three diseases affecting grapes and identifying the severity of these diseases using image processing and machine learning algorithms, and achieves best classification accuracy of 86% using Random Forest and GLCM features.
AN EFFICIENT DISEASE DIAGNOSTIC AND TREATMENT SYSTEM FOR COTTON PLANT USING DIGITAL IMAGE PROCESSING
TLDR
An approach that regularizes and extracts eigen feature from cotton leaf image and diagnostic a diseases like Bacterial leaf blight, Red Leaf Blight, Black Spot, fungus, Anthracnose, Red Spots, White Spots is exposed.
Disease Detection and Diagnosis on Plant using Image Processing - A Review
TLDR
There are different feature extraction techniques for extracting the color, texture and edge features such as color space, color histogram, grey level co-occurrence matrix (CCM), Gabor filter, Canny and Sobel edge detector.
Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System
TLDR
This work focuses on Grapes plant leaf disease detection system, which takes a single leaf of a plant as an input and segmentation is performed after background removal, and classifies focus on downy mildew & black rot.
Plant disease detection using computational intelligence and image processing
TLDR
Common infections along with the research landscape at different stages of such detection systems are discussed and the modern feature extraction techniques are analyzed for identifying those that appear to work well covering several crop categories.
Recognition of Diseases of Leaf using SVM with Radial Basis Kernel Function
  • Anuradha Sharma
  • Mathematics
    International Journal for Research in Applied Science and Engineering Technology
  • 2019
: Organic farming obtains admiration in agricultural industry. Several complications get up during farming wherein the disease affected leaf contemplated to be very powerful aspect for the shortage
...
...

References

SHOWING 1-10 OF 31 REFERENCES
Features selection of cotton disease leaves image based on fuzzy feature selection techniques
TLDR
The results show that the effectiveness of features selected by the FC and FS method is much better than that selected by human randomly or other methods.
Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features
TLDR
The proposed algorithm’s efficiency can successfully detect and classify the examined diseases with an accuracy of 94%.
Cotton Pests and Diseases Detection Based on Image Processing
Extract the damaged image form the cotton image in order to measure the damage ratio of the cotton leaf which caused by the diseases or pests. Several algorithms like image enhancement, image
Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering
TLDR
The paper will proposed the technique for feature extraction and comparison of two techniques, and changes in the color are a valuable indicator of crop health, and Digital Analysis of crop color is the important.
Grape leaf disease detection from color imagery using hybrid intelligent system
TLDR
This work presents automatic plant disease diagnosis using multiple artificial intelligent techniques and shows desirable results which can be further developed for any agricultural product analysis/inspection system.
Identification of foliar diseases in cotton crop
The manifestation of pathogens in plantations is the most important cause of losses in several crops. These usually represent less income to the farmers due to the lower product quality as well as
Disease Detection On Cotton Leaves by Eigenfeature Regularization and Extraction Technique
TLDR
A approach that regularizes and extracts eigenfeature from cotton leaf image and enables the discriminant evaluation performed in the whole space feature extraction or dimensionality reduction occurs at the final stage, after comparison of this feature results to disease identification.
WEB-Based Intelligent Diagnosis System for Cotton Diseases Control
TLDR
A WEB-based Intelligent Diagnosis System for Cotton Diseases Control was developed and showed the rate of correctness that system could identify the symptom was 89.5% in average, and the average running time for a diagnosis was 900ms.
An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers
TLDR
This paper proposes a Support Vector Machines based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease and provides the implementation details along with the corresponding results.
Feature Selection: Evaluation, Application, and Small Sample Performance
TLDR
This work studies the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models and shows that pooling features derived from different texture Models, followed by a feature selection results in a substantial improvement in the classification accuracy.
...
...