scholarly journals Classification Techniques for Plant Disease Detection

2020 ◽  
Vol 8 (6) ◽  
pp. 5423-5430
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Early Stage ◽  
Support Vector ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Traditional Approaches

Production of crops with better quality is the necessary attribute for the economic growth of any country. The agricultural sector provides employment to many people and accounts for major portion of gross domestic product in many countries around the world. Therefore, for enhanced agricultural productivity the detection of diseases in plants at an early stage is quite significant. The traditional approaches for disease detection in plants required considerable amount of time, intense research, and constant monitoring of the farm. However, optimized solutions have been obtained over the past few years due to technological advances that have resulted in better yields for the farmers. Machine learning and image processing are used to detect the disease on the agricultural harvest. The image processing steps for plant disease identification include acquiring of images, pre-processing, segmentation and feature extraction. In this review paper, we focused mainly on the most utilized classification mechanisms in disease detection of plants such as Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network. It has been observed from the analysis that Convolutional Neural Network approach provides better accuracy compared to the traditional approaches.

scholarly journals A Deep Learning Method for Prediction of Cardiovascular Disease Using Convolutional Neural Network

2020 ◽  
Vol 34 (5) ◽  
pp. 601-606
Author(s):  
Tulasi Krishna Sajja ◽  
Hemantha Kumar Kalluri
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Traditional Approaches

Heart disease is a very deadly disease. Worldwide, the majority of people are suffering from this problem. Many Machine Learning (ML) approaches are not sufficient to forecast the disease caused by the virus. Therefore, there is a need for one system that predicts disease efficiently. The Deep Learning approach predicts the disease caused by the blocked heart. This paper proposes a Convolutional Neural Network (CNN) to predict the disease at an early stage. This paper focuses on a comparison between the traditional approaches such as Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Networks (NN), and the proposed prediction model of CNN. The UCI machine learning repository dataset for experimentation and Cardiovascular Disease (CVD) predictions with 94% accuracy.

scholarly journals Fruit Disease Detection Using Convolution Neural Network Approach

2018 ◽  
Vol 7 (2) ◽  
pp. 62-65
Author(s):  
Shivani . ◽  
Sharanjit Singh
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Texture Features ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Disease Information ◽  
Fruit Disease ◽  
Farming Industry

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

scholarly journals Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Morteza Amini ◽  
MirMohsen Pedram ◽  
AliReza Moradi ◽  
Mahshad Ouchani
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Early Stage ◽  
Support Vector ◽  
Feature Extraction Method

The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

scholarly journals Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network

2019 ◽  
Vol 10 (2) ◽  
pp. 98-110 ◽  
Author(s):  
Keke Zhang ◽  
Lei Zhang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Support Vector ◽  
Automatic Identification ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leaf Disease

The cherry leaves infected by Podosphaera pannosa will suffer powdery mildew, which is a serious disease threatening the cherry production industry. In order to identify the diseased cherry leaves in early stage, the authors formulate the cherry leaf disease infected identification as a classification problem and propose a fully automatic identification method based on convolutional neural network (CNN). The GoogLeNet is used as backbone of the CNN. Then, transferred learning techniques are applied to fine-tune the CNN from pre-trained GoogLeNet on ImageNet dataset. This article compares the proposed method against three traditional machine learning methods i.e., support vector machine (SVM), k-nearest neighbor (KNN) and back propagation (BP) neural network. Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%. Thus, a CNN can be used effectively in identifying the diseased cherry leaves.

scholarly journals A convolutional neural network approach for visual recognition in wheel production lines

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092687
Author(s):  
Zheming Tong ◽  
Jie Gao ◽  
Shuiguang Tong
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Automobile Industry ◽  
Visual Recognition ◽  
Network Approach ◽  
Production Lines ◽  
Neural Network Approach ◽  
Automotive Manufacturing ◽  
Actual Production

China has been the world’s largest automotive manufacturing country since 2008. The automotive wheel industry in China has been growing steadily in pace with the automobile industry. Visual recognition system that automatically classifies wheel types is a key component in the wheel production line. Traditional recognition methods are mainly based on extracted feature matching. Their accuracy, robustness, and processing speed are often compromised considerably in actual production. To overcome this problem, we proposed a convolutional neural network approach to adaptively classify wheel types in actual production lines with a complex visual background. The essential steps to achieve wheel identification include image acquisition, image preprocessing, and classification. The image differencing algorithm and histogram technique are developed on acquired wheel images to remove track disturbances. The wheel images after image processing were organized into training and test sets. This approach improved the residual network model ResNet-18 and then evaluated this model based on the wheel test data. Experiments showed that this method can obtain an accuracy over 98% on nearly 70,000 wheel images and its single image processing time can reach millisecond level.

scholarly journals Plant Leaf Diseases Prediction and Classification Using Optimization Based Convolution Neural Network

2020 ◽  
pp. 174-176
Author(s):  
Mohan M ◽  
Vijayaganth V ◽  
Naveenkumar M
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Network Approach ◽  
Plant Leaves ◽  
Neural Network Approach ◽  
Plant Leaf ◽  
Network Support ◽  
Highlight Extraction

Plant leaf diseases and ruinous bugs are a significant test in the horticulture area. Quicker and an exact forecast of leaf diseases in plant could assist with building up an early treatment strategy while extensively decreasing financial misfortunes. Current progressed advancements in profound learning permitted analysts to amazingly improve the presentation and exactness of article identification and acknowledgment frameworks. A profound learning-based way to deal with recognize leaf illnesses in various plants utilizing pictures of plant leaves. The picture handling ventures for plant illness recognizable proof incorporate obtaining of pictures, pre-preparing, division and highlight extraction. Focus in predominantly on the most used order systems in illness location of plants, for example, Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network. It has been seen from the examination that advancement Convolutional Neural Network approach gives better precision contrasted with the conventional methodologies. Optimization based CNN convolution neural network the proposed framework can viably recognized various sorts of diseases with the capacity to manage complex situations from a plant's region.

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