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.

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 KLASIFIKASI KUALITAS KAYU KELAPA MENGGUNAKAN ARSITEKTUR CNN

2021 ◽  
Vol 8 (1) ◽  
pp. 49
Author(s):  
Nurul Fathanah Mustamin ◽  
Yuslena Sari ◽  
Husnul Khatimi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Computer Visualization ◽  
Average Accuracy ◽  
Export Volume ◽  
The Right ◽  
Visualization Techniques

<p><em>The increase in the export volume of coconut logs, which are materials that can efficiently substitute for conventional wood, demands that the quality of coconut wood classified quickly. However, due to the limitations of a grader as a human being, it is necessary to have assistance from machines or technology that can classify coconut wood quickly. Techniques that used for rapid classification can use computer visualization. Convolutional Neural Network (CNN) with the right architecture makes this method able to recognize and detect objects well, which influenced by computerized factors, large datasets, and techniques to train deeper networks. This study uses </em><em>five</em><em> types of CNN architecture, AlexNet, GoogLeNet, ResNet101, ResNet18, and ResNet50. The research results obtained for the classification of the quality of coconut wood using </em><em>images </em><em>show that the GoogLeNet architecture has the best classification performance among other architectures</em><em>. </em><em>GoogLeNet</em><em> gets result</em><em> with an average accuracy of 84.89% in each layer, followed by RestNet101 architecture with an average accuracy of 78.41%, RestNet50 with an average accuracy of 77.18%, RestNet18 with an average accuracy of 72.94% and the lowest accuracy performance among other architectures obtained by AlexNet with an average accuracy of 65.84%.</em></p><p><em><strong>Keywords</strong></em><em>: Classification, Coconut Wood, Computer Visualization Techniques, CNN</em> </p><p><em>Meningkatnya volume ekspor kayu kelapa yang merupakan bahan pengganti kayu konvensional secara efisien menuntut klasifikasi kualitas kayu kelapa dengan cepat. Namun karena keterbatasan seorang grader sebagai manusia maka diperlukan bantuan mesin atau teknologi yang dapat mengklasifikasikan kayu kelapa dengan cepat. Teknik yang dapat digunakan untuk klasifikasi cepat dapat menggunakan teknik visualisasi komputer. Convolutional Neural Network (CNN) dengan arsitektur yang tepat menjadikan metode ini mampu mengenali dan mendeteksi objek dengan baik, yang sebagian besar dipengaruhi oleh faktor komputerisasi, dataset yang besar, dan teknik untuk melatih jaringan yang lebih dalam. Penelitian ini menggunakan lima jenis arsitektur CNN yaitu, AlexNet, GoogLeNet, ResNet101, ResNet18, dan ResNet50. Hasil penelitian yang diperoleh untuk klasifikasi kualitas kayu kelapa menggunakan citra menunjukkan bahwa arsitektur GoogLeNet memiliki performansi klasifikasi terbaik diantara arsitektur lainnya. GoogLeNet mendapatkan hasil dengan rata-rata akurasi 84,89% pada setiap lapisan, disusul arsitektur RestNet101 dengan akurasi rata-rata 78,41%, RestNet50 dengan akurasi rata-rata 77,18%, RestNet18 dengan akurasi rata-rata 72,94% dan kinerja akurasi terendah di antara arsitektur lainnya diperoleh AlexNet dengan akurasi rata-rata 65,84%.</em></p><p><em><strong>Kata kunci</strong></em><em>: Klasifikasi, Kayu Kelapa, Teknik Visualisasi Komputer, CNN</em></p>

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

Sign in / Sign up

Export Citation Format

Share Document