A Novel Disease Detection and Classification Method Using Improved Fusion Random Weight Support Vector Machine

2021 ◽  
Vol 11 (12) ◽  
pp. 2976-2986
Author(s):  
M. Usha Rani ◽  
N. Saravana Selvam
Keyword(s):  
Support Vector Machine ◽  
Human Health ◽  
Health Informatics ◽  
Research Work ◽  
Noise Removal ◽  
Support Vector ◽  
Computational Techniques ◽  
Disease Detection ◽  
Successful Implementation ◽  
Leaf Disease

Health informatics is one of the main branch of engineering which provides a solution to a variety of problems like delayed, missed or incorrect diagnoses with the help of computational techniques. With the help of technologies such as bio-computing, health informatics, the disaster impacts on both human health and biological factors can be reduced to a large extend. Using these computational technologies, the country’s economy can also get boosted up and due to increased disease-causing pathogens, which directly impact the human health system. In this research work, a different type of sugarcane disease is detected and classified because manual identification is difficult and time-consuming. So, the farmers couldn’t find a better solution, than on the whole, they go for stubble burning, which is an alarming issue both on human and environmental wellness. The burning of bagasse causes bagassois, an interstitial lung disease that affects the tissues present in the lung through the air sacs. So, this sugarcane disease detection needs to be done early to avoid various health and environmental issues. The proposed work consists of the detection of four types of sugarcane leaf disease directly from the field. The sequence of methods is capturing images with WSN nodes, pre-processing with image enhancement and noise removal (IENR), segmentation with Fuzzy membership function and clustering (FMFC), feature extraction using Gray Level Co-occurrence Matrix Vector (GLCMV) and classification using Support Vector Machine (SVM). With the help of the effective proposed method, the highest parameters like precision, accuracy, sensitivity, and specificity for sugarcane leaf disease have been obtained. Based on the successful implementation process, the accuracy stated for the four sugarcane diseases along with the execution time is given below as Smut disease (87.12, 1.01 sec), Rust disease (90.23, 1.02 sec), Grassy Shoot disease (95.34, 1.047 sec), Red Rot disease (95.51, 1.04 sec).

scholarly journals A Hybrid Leaf Disease Detection Scheme using Grayco-Occurance Matrix Support Vector Machine Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 300-309
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Median Filter ◽  
Noise Removal ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Detection Scheme ◽  
Leaf Disease ◽  
Fuzzy C Means Clustering ◽  
Industry Area

An Indian economy depends upon the agriculture up to 70% approximately. Hence, there is a need to Take care of agriculture and its resources. In such aspects, the plant disease and leaf disease is one of the major concerns that affect the overall processing of producing food, feed, fiber and many other favorite products by the cultivation. It is one of the reasons that disease identification and detection in plant adopts a significant job in agro industry area. Due to this reason, appropriate detection methodology consideration is to be taken here. Most of the research focused more on combining image processing and soft computing algorithms to solve this issue. With this motivation, this research utilize Median filter for noise removal in initial stage. Later, Hue-Saturation-Value is used for preprocessing. Further, Fuzzy C-Means Clustering (FCM) considered for clustering image samples at different iteration. Finally, the research considered a hybrid mechanism by combining Gray Co-Occurrence Matrix and Support Vector Machine. Further, the proposed method results better outcome in terms of efficiency as 87.43% K-nearest neighbor (KNN) classifier, Color Transform and Exponential Spider Monkey Optimization.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

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