scholarly journals Classification of Plants Leaf Diseases using Convolutional Neural Network

2021 ◽  
Vol 24 (2) ◽  
pp. 64-71
Author(s):  
Reem Mohammed Jasim Al-Akkam ◽  
◽  
Mohammed Sahib Mahdi Altaei ◽  
Keyword(s):  
Neural Network ◽  
Agricultural Production ◽  
Optimization Technique ◽  
Plant Diseases ◽  
Cross Entropy ◽  
Training Phase ◽  
Proposed Model ◽  
Rgb Images ◽  
Deep Cnn

Agriculture is one of the most important professions in many countries, including Iraq, as the Iraqi financial system depends on agricultural production and great attention should be paid to concerns about agricultural production. Because plants are exposed to many diseases and monitoring plant diseases with the help of specialists in the agricultural region can be very expensive. There is a need for a system capable of automatically detecting diseases. The aim of the research proposed is to create a model that classifies and predicts leaf diseases in plants. This model is based on a convolution network, which is a kind of deep learning. The dataset used in this study called (Plant Village) was downloaded from the kaggle website. The dataset contains 34,934 RGB images, and the deep CNN model can efficiently classify 15 different classes of healthy and diseased plants using the leaf images. The model used techniques to augment data and dropout. The Soft max output layer was used with the categorical cross-entropy loss function to apply the CNN model proposed with the Adam optimization technique. The results obtained by the proposed model were 97.42% in the training phase and 96.18% in the testing phase.

scholarly journals A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

2020 ◽  
Vol 37 (6) ◽  
pp. 1093-1101
Author(s):  
Divakar Yadav ◽  
Akanksha ◽  
Arun Kumar Yadav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Daily Intake ◽  
Plant Diseases ◽  
Data Set ◽  
Proposed Model ◽  
Fcm Clustering ◽  
Contrast Stretching

Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

2020 ◽  
Vol 65 (6) ◽  
pp. 759-773
Author(s):  
Segu Praveena ◽  
Sohan Pal Singh
Keyword(s):  
Neural Network ◽  
Acute Lymphoblastic Leukaemia ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lymphoblastic Leukaemia ◽  
Input Image ◽  
Deep Convolutional Neural Network ◽  
Grey Wolf ◽  
Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

2021 ◽  
Vol 16 ◽  
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Melting Curve ◽  
Classification Model ◽  
Proposed Model ◽  
Unbalanced Dataset ◽  
Manual Selection ◽  
Low Efficiency ◽  
Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

Plant Disease Detection and Classification Using Bacteria Foraging Optimization Algorithm Through Convolution Neural Network

2020 ◽  
Vol 17 (8) ◽  
pp. 3567-3576
Author(s):  
Venigalla Sai Teja ◽  
Chilakapati Srinivas ◽  
P. Radhika
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Euclidean Distance ◽  
Plant Diseases ◽  
Prompt Action ◽  
The Right ◽  
Measurement Metric ◽  
Bacteria Foraging Optimization Algorithm ◽  
Advanced Model

Humans can recognize the plants infected by diseases but separated from our visual perception it is hard to recognize plant diseases. In croplands without taking the right care and prompt action, the entire field may become a region afflicted by diseases. So we identify the plant diseases ahead of time with the assistance of present-day computer technologies. An advanced model was introduced to accurately recognize and classification plant diseases. Here we proposed an approach that can use the Convolutional Neural Network (CNN) based on BFOA for distinguishing diseases in plants. The input picture for the extraction of features is divided into 3 clusters by the Euclidean distance measurement metric of the k-mean algorithm and from the ROI, parameters of the GLCM matrix are calculated in the same cluster prior to BFOA. Assigning matrix parameters as BFOA input improves the network’s accuracy and efficiency in determining. In classification, we proposed a Convolutional Neural Network (CNN) using ResNet50 as a pre-trained network in deep learning toolbox which classifies from a given dataset. The approach is more reliable as the detection and classification of plant diseases are more precise.

Identification of Tomato Leaf Disease Detection using Pretrained Deep Convolutional Neural Network Models

2020 ◽  
Vol 21 (4) ◽  
pp. 625-635
Author(s):  
Anandhakrishnan T ◽  
Jaisakthi S.M Murugaiyan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Computing Time ◽  
Network Models ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Leaf Disease ◽  
Proposed Model ◽  
Deep Cnn

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability. The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decreasein quality. And Xception also generated a fine 99.45% precision in less computing time.

scholarly journals Leaf Disease Identification using Convolution Neural Network (CNN)

2020 ◽  
Vol 9 (9) ◽  
pp. 477-481
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Tomato Plant ◽  
Extraction Methods ◽  
Plant Diseases ◽  
Viral Diseases ◽  
Leaf Disease ◽  
Proposed Model ◽  
Reliable Performance ◽  
First Time

The tomato plant is the most broadly cultivated produce in India. As the Convolutional Neural Network (CNN) which comes under the field of image classification is performing the progressive work, thus using an approach of deep learning which mainly centers on achieving high accuracy of leaf disease of the tomato plant. Therefore, the main objective of this paper is to acquire more reliable performance in the identification of diseases. Amidst various plant diseases that affect leaf comprise of Late blight, bacterial and viral diseases have been chosen to differentiate infected leaves from that of the healthy leaves includes Late blight, bacterial and viral diseases. As we know, none of the other method has been proposed earlier which helps in detecting plant leaf diseases for the first time. Hence the proposed model is designed in such a way that it effectively identifies specific diseases that affect leaves of tomato plants through the use of a dataset containing about 4000 leaf images. CNN achieves an overall accuracy of 96% without implementing any pre-processing and feature extraction methods.

scholarly journals Performance Measures of Respiratory Disease Classification using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 884-892
Author(s):  
G. Rama Janani
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Performance Measures ◽  
Respiratory Infections ◽  
Respiratory Illness ◽  
Extraction Methods ◽  
Disease Classification ◽  
Deep Cnn

The paper is based on classification of respiratory illness like covid 19 and pneumonia by using deep learning. The symptoms of COVID-19 and pneumonia are similar. Due to this, it is often difficult to identify what is causing your condition without being tested for COVID-19 or other respiratory infections. To find out how COVID-19 and pneumonia differs from one another, this paper presents that a novel Convolutional Neural Network in Tensor Flow and Keras based Covid-19 pneumonia classification. The proposed system supported implements CNN using Pneumonia images to classify the Covid-19, normal, pneumonia. The knowledge from these studies can potentially help in diagnosis of the concerned disease. It is predicted that the success of the anticipated results will increase if the CNN method is supported by adding extra feature extraction methods for classifying covid-19 and pneumonia successfully thereby improving the efficacy and potential of using deep CNN to pictures.

Neural-network-based classification of skin structural elements in case of urticaria using histological RGB images

2020 ◽  
Author(s):  
Sergey Shirokov ◽  
Artem Borbat ◽  
Olga Polschikova ◽  
Ekaterina Lovchikova ◽  
Inna Danilycheva ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Elements ◽  
Rgb Images
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