scholarly journals Analysis of New RGB Vegetation Indices for PHYVV and TMV Identification in Jalapeño Pepper (Capsicum annuum) Leaves Using CNNs-Based Model

Plants ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1977
Author(s):  
Arturo Yee-Rendon ◽  
Irineo Torres-Pacheco ◽  
Angelica Sarahy Trujillo-Lopez ◽  
Karen Paola Romero-Bringas ◽  
Jesus Roberto Millan-Almaraz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Capsicum Annuum ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Complete Analysis ◽  
Classification Models ◽  
Learning Models ◽  
Processing Algorithms ◽  
Learning Architectures

Recently, deep-learning techniques have become the foundations for many breakthroughs in the automated identification of plant diseases. In the agricultural sector, many recent visual-computer approaches use deep-learning models. In this approach, a novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and Pepper Huasteco Yellow Vein Virus (PHYVV) visual symptoms on Jalapeño pepper (Capsicum annuum L.) leaves by using image-processing and deep-learning classification models is presented. The proposed image-processing approach is based on the utilization of Normalized Red-Blue Vegetation Index (NRBVI) and Normalized Green-Blue Vegetation Index (NGBVI) as new RGB-based vegetation indices, and its subsequent Jet pallet colored version NRBVI-Jet NGBVI-Jet as pre-processing algorithms. Furthermore, four standard pre-trained deep-learning architectures, Visual Geometry Group-16 (VGG-16), Xception, Inception v3, and MobileNet v2, were implemented for classification purposes. The objective of this methodology was to find the most accurate combination of vegetation index pre-processing algorithms and pre-trained deep- learning classification models. Transfer learning was applied to fine tune the pre-trained deep- learning models and data augmentation was also applied to prevent the models from overfitting. The performance of the models was evaluated using Top-1 accuracy, precision, recall, and F1-score using test data. The results showed that the best model was an Xception-based model that uses the NGBVI dataset. This model reached an average Top-1 test accuracy of 98.3%. A complete analysis of the different vegetation index representations using models based on deep-learning architectures is presented along with the study of the learning curves of these deep-learning models during the training phase.

scholarly journals Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements

2021 ◽  
Vol 13 (22) ◽  
pp. 4560
Author(s):  
Lili Luo ◽  
Qingrui Chang ◽  
Qi Wang ◽  
Yong Huang
Keyword(s):  
Mosaic Virus ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Biochemical Parameter ◽  
Maize Dwarf Mosaic Virus ◽  
Support Vector ◽  
Classification Models ◽  
Linear Discriminant ◽  
And Control

Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (Rλ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VIs) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VIc) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on Rλ, VIs, VIc, and Rλ + VIs + VIc and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VIc-based classification models was 100%, which is significantly higher than that of the VIs and Rλ-based models. Among the Anth regression models, the accuracy of the MLR model based on Rλ + VIs + VIc was the highest (R2c = 0.85; R2v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.

scholarly journals A Novel Deep Learning Based Sentiment Analysis of Movie using Hybrid CNN_SVM Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 12391-12394
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Business Strategy ◽  
Performance Metrics ◽  
Classification Models ◽  
Learning Models ◽  
Meaningful Information

Data flow in web is becoming high and vast, extracting useful and meaningful information from the same is especially significant. The extracted information can be utilized for enhanced decision making. The information provided by the end-users is normally in the form of comments with respect to different products and services. Sentiment analysis is effectively carried out in these kinds of compact review to give away the people’s opinion of any products. This analyzed data will be efficient to improve the business strategy. In our work the collected online movie reviews are analyzed by using machine learning sentiment classification models like Random Forest, Naive Bayes, KNN and SVM. The work has been extended with CNN and hybrid CNN-SVM deep learning models to achieve higher performance. Comparing the workings of all the above classification models for sentiment analysis based upon various performance metrics is the main objective of the paper.

scholarly journals Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1932
Author(s):  
Malik Haris ◽  
Adam Glowacz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Single Shot ◽  
Automated Driving ◽  
Convolutional Network ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

scholarly journals Malware Detection using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1847-1853
Author(s):  
T. Shiva Rama Krishna
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Image Processing Technique ◽  
Feature Engineering ◽  
Public And Private ◽  
Static And Dynamic Analysis ◽  
Private Datasets ◽  
Learning Architectures

Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently in order to arrive at new enhanced methods for effective zero-day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each other’s and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing-based hybrid approach in a big data environment is a new enhanced method for effective zero-day malware detection.

scholarly journals Comparative Analysis of Deep Learning Image Detection Algorithms

2020 ◽  
Author(s):  
Shrey Srivast ◽  
Amit Vishvas Divekar ◽  
Chandu Anilkumar ◽  
Ishika Naik ◽  
Ved Kulkarni ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Visual Media ◽  
Image Detection ◽  
Detection Algorithms ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
The Moment

Abstract As humans, we do not have to strain ourselves when we interpret our surroundings through our visual senses. From the moment we begin to observe, we unconsciously train ourselves with the same set of images. Hence, distinguishing entities is not a difficult task for us. On the contrary, computer views all kinds of visual media as an array of numerical values. Due to this contrast in approach, they require image processing algorithms to examine the contents of images. This project presents a comparative analysis of 3 major image processing algorithms: SSD, Faster R-CNN, and YOLO. In this analysis, we have chosen the COCO dataset. With the help of the COCO dataset, we have evaluated the performance and accuracy of the three algorithms and analysed their strengths and weaknesses. Using the results obtained from our implementations, we determine the differences between how each algorithm runs and suitable applications for each. The parameters for evaluation are accuracy, precision, F1 score.

scholarly journals Plataforma para apoio a modelagem de próteses com base em Processamento Digital de Imagens e Deep Learning

2021 ◽  
Author(s):  
Bianka Tallita Passos ◽  
Moira Cristina Cubas Fatiga Tillmann ◽  
Anita Maria da Rocha Fernandes
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
High Resolution ◽  
Image Classification ◽  
Digital Image Processing ◽  
Medical Practice ◽  
Digital Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Specific Label

Medical practice in general, and dentistry in particular, generatesdata sources, such as high-resolution medical images and electronicmedical records. Digital image processing algorithms takeadvantage of the datasets, enabling the development of dental applicationssuch as tooth, caries, crown, prosthetic, dental implant, andendodontic treatment detection, as well as image classification. Thegoal of image classification is to comprehend it as a whole and classifythe image by assigning it to a specific label. This work presentsthe proposal of a tool that helps the dental prosthesis specialist toexchange information with the laboratory. The proposed solutionuses deep learning to classify image, in order to improve the understandingof the structure required for modeling the prosthesis. Theimage database used has a total of 1215 images. Of these, 60 wereseparated for testing. The prototype achieved 98.33% accuracy.

scholarly journals The Use of Features from Fluorescence, Thermography, and NDVI Imaging to Detect Biotic Stress in Lettuce

Plant Disease ◽  
2018 ◽  
Vol 102 (6) ◽  
pp. 1101-1107 ◽  
Author(s):  
Martin Sandmann ◽  
Rita Grosch ◽  
Jan Graefe
Keyword(s):  
Image Processing ◽  
Biotic Stress ◽  
Thermal Imaging ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Quantum Yield ◽  
Whole Plant ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Horticultural Practice

Fluorescence, normalized difference vegetation index, and thermal imaging are three frequently used nondestructive methods to detect biotic stress in plants. Due, in part, to the inconsistent results reported in the literature and the lack of measurements on the whole-plant scale, we tested the suitability of a wide variety of variables obtained using these three imaging methods to classify young plants into biotically stressed and nonstressed plants. To this end, we applied the model plant–pathogen system lettuce–Rhizoctonia solani. The relevant data from each image and plant (healthy and diseased) was extracted semiautomatically using sophisticated image processing algorithms. This method enabled us to identify the most appropriate variables via discriminant function and logistic regression analysis: photosystem II maximum quantum yield (Fv/Fm) and fluorescence decline ratio can be used to classify variables with an error ≤0.052. Lettuce seedlings with an Fv/Fm ratio > 0.73 were consistently healthy. In some cases, it was possible to detect infection prior to the appearance of symptoms. Possibilities to transfer the method to horticultural practice are discussed.

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