Smart paddy field monitoring system using deep learning and IoT

2021 ◽  
pp. 1063293X2198894
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Nithiyakanthan Kannan ◽  
Sridevi Narayanan ◽  
Chanki Pandey
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Paddy Field ◽  
New Technology ◽  
Support Vector ◽  
Nitrogen Status ◽  
Deep Feature ◽  
Per Capita ◽  
Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

scholarly journals A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps

2020 ◽  
Vol 25 (3) ◽  
pp. 373-382
Author(s):  
He Yu ◽  
Zaike Tian ◽  
Hongru Li ◽  
Baohua Xu ◽  
Guoqing An
Keyword(s):  
Deep Learning ◽  
Graph Model ◽  
Parameter Tuning ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Deep Feature ◽  
Proposed Model ◽  
Vector Machines ◽  
Deep Feature Extraction

Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.

The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images

2021 ◽  
pp. 290-309
Author(s):  
Aras Masood Ismael ◽  
Juliana Carneiro Gomes
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fine Tuning ◽  
Svm Classifier ◽  
X Ray ◽  
Deep Feature ◽  
Deep Cnn ◽  
End To End ◽  
Deep Feature Extraction

In this chapter, deep learning-based approaches, namely deep feature extraction, fine-tuning of pre-trained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, are used to classify the malignant and normal breast X-ray images. For deep feature extraction, pre-trained deep CNN models such as ResNet18, ResNet50, ResNet101, VGG16, and VGG19 are used. For classification of the deep features, the support vector machines (SVM) classifier is used with various kernel functions namely linear, quadratic, cubic, and Gaussian, respectively. The aforementioned pre-trained deep CNN models are also used in fine-tuning procedure. A new CNN model is also proposed in end-to-end training fashion. The classification accuracy is used as performance measurements. The experimental works show that the deep learning has potential in detection of the breast cancer from the X-ray images. The deep features that are extracted from the ResNet50 model and SVM classifier with linear kernel function produced 94.7% accuracy score which the highest among all obtained.

Developing Brain Tumor Detection Model Using Deep Feature Extraction via Transfer Learning

2021 ◽  
pp. 119-137
Author(s):  
Adem Assfaw Mekonnen ◽  
Hussien Worku Seid ◽  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Transfer Learning ◽  
Intensity Variation ◽  
Machine Learning Algorithms ◽  
Proposed Model ◽  
Deep Feature Extraction

The timely prognosis of brain tumors is gambling a great role within the pretreatment of patients and keep the life of suffers. The manual classification of brain tumors is a difficult task for radiologists due to the intensity variation pixel information produced by the magnetic resonance machine and it is a very tedious task for a large number of images. A deep learning algorithm becomes a famous algorithm to conquer the problems traditional machine learning algorithms by automatically feature extraction from the input spaces and accurately detect the brain tumors. One of the most important features of deep learning is transferred a gain knowledge strategy to use small datasets. Transfer learning is explored by freezing layers and fine-tuning a pre-trained model to a recommended convolutional neural net model. The proposed model is trained using 4000 real magnetic resonance images datasets. The mean accuracy of the proposed model is found to be 98% for brain tumor classifications with mini-batch size 32 and a learning rate of 0.001.

scholarly journals Script Identification in Handwritten Documents for Gurumukhi-Latin Script Using Transfer Learning with Deep and Shallow Classifiers

2021 ◽  
Author(s):  
Sukhandeep Kaur ◽  
Seema Bawa ◽  
Ravinder Kumar
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Gabor Filter ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Handwritten Documents ◽  
Feature Sets ◽  
Script Identification ◽  
Deep Networks

Abstract Script identification at character level in handwritten documents is a challenging task for Gurumukhi and Latin scripts due to the presence of slightly similar, quite similar or at times confusing character pairs. Hence, it is found to be inadequate to use single feature set or just traditional feature sets and classifier in processing the handwritten documents. Due to the evolution of deep learning, the importance of traditional feature extraction approaches is somewhere neglected which is considered in this paper. This paper investigates machine learning and deep learning ensemble approaches at feature extraction and classification level for script identification. The approach here is: i. combining traditional and deep learning based features ii. evaluating various ensemble approaches using individual and combined feature sets to perform script identification iii. evaluating the pre-trained deep networks using transfer learning for script identification ’iv. finding the best combination of feature set and classifiers for script identification. Three different kinds of traditional features like Gabor filter, Gray Level Co-Occurrence Matrix (GLCM), Histograms of Oriented Gradiants (HOG) are employed. For deep learning pretrained deep networks like VGG19, ResNet50 and LeNet5 have been used as feature extractor. These individual and combined features are trained using classifiers like Support Vector Machines (SVM) , K nearest neighbor (KNN), Random Forest (rf) etc. Further many ensemble approaches like Voting,Boosting and Bagging are evaluated for script classification. Exhaustive experimental work resulted into the highest accuracy of 98.82% with features extracted from ResNet50 using transfer learning and bagging based ensemble classifier which is higher as compared to previously reported work.

scholarly journals Computer Aid Screening of COVID19 using X-ray and CT Scan Images: A Comparative Study

2020 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Chanki Pandey ◽  
Santi Kumari Behera
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Ct Scan ◽  
Transfer Learning ◽  
Screening Method ◽  
Learning Approaches ◽  
X Ray ◽  
Computer Aid ◽  
Deep Feature ◽  
Deep Feature Extraction

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

scholarly journals Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison

2021 ◽  
pp. 1-14
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Komma Anitha ◽  
Chanki Pandey ◽  
M.R. Khan
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Transfer Learning ◽  
Classification Models ◽  
X Ray ◽  
Computer Aid ◽  
Deep Feature ◽  
Chest X Ray ◽  
Image Set ◽  
Deep Feature Extraction

The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.

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