scholarly journals Tomato Leaf Diseases Detection Using Deep Learning Technique

2021 ◽  
Author(s):  
Muhammad E.H. Chowdhury ◽  
Tawsifur Rahman ◽  
Amith Khandakar ◽  
Nabil Ibtehaz ◽  
Aftab Ullah Khan ◽  
...  
Keyword(s):  
Binary Classification ◽  
Experimental Studies ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Superior Performance ◽  
Disease Monitoring ◽  
World Population ◽  
Network Architectures ◽  
Comparative Performance ◽  
Human Monitoring

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. DenseNet201 also outperform for six-class classification with an accuracy of 97.99%. Finally, DenseNet201 achieved an accuracy of 98.05% for ten-class classification. It can be concluded that deep architectures performed better at classifying the diseases for the three experiments. The performance of each of the experimental studies reported in this work outperforms the existing literature.

scholarly journals Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques

2021 ◽  
Vol 3 (2) ◽  
pp. 294-312
Author(s):  
Muhammad E. H. Chowdhury ◽  
Tawsifur Rahman ◽  
Amith Khandakar ◽  
Mohamed Arselene Ayari ◽  
Aftab Ullah Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Experimental Studies ◽  
Plant Diseases ◽  
Superior Performance ◽  
World Population ◽  
Comparative Performance ◽  
Learning Techniques ◽  
Segmented Images ◽  
Segmentation Models

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature.

scholarly journals Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images

2016 ◽  
Author(s):  
Muhammad Yousefnezhad ◽  
Daoqiang Zhang
Keyword(s):  
Human Brain ◽  
Pattern Analysis ◽  
Binary Classification ◽  
Visual Stimuli ◽  
Experimental Studies ◽  
Active Regions ◽  
Superior Performance ◽  
Class Prediction ◽  
Feature Extraction Method ◽  
Adaboost Algorithm

AbstractA universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noises in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.

scholarly journals Evolution of ribosomal protein network architectures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youri Timsit ◽  
Grégoire Sergeant-Perthuis ◽  
Daniel Bennequin
Keyword(s):  
Selective Pressure ◽  
Experimental Studies ◽  
Protein Networks ◽  
Network Architectures ◽  
Mathematical Study ◽  
Remote Communication ◽  
Aromatic Residues ◽  
Peptidyl Transfer ◽  
Exit Tunnel ◽  
Long Range Communication

AbstractTo perform an accurate protein synthesis, ribosomes accomplish complex tasks involving the long-range communication between its functional centres such as the peptidyl transfer centre, the tRNA bindings sites and the peptide exit tunnel. How information is transmitted between these sites remains one of the major challenges in current ribosome research. Many experimental studies have revealed that some r-proteins play essential roles in remote communication and the possible involvement of r-protein networks in these processes have been recently proposed. Our phylogenetic, structural and mathematical study reveals that of the three kingdom’s r-protein networks converged towards non-random graphs where r-proteins collectively coevolved to optimize interconnection between functional centres. The massive acquisition of conserved aromatic residues at the interfaces and along the extensions of the newly connected eukaryotic r-proteins also highlights that a strong selective pressure acts on their sequences probably for the formation of new allosteric pathways in the network.

Author Reputation Measurement on Question and Answer Sites by the Classification of Author-Generated Content

2021 ◽  
Vol 31 (10) ◽  
pp. 1421-1445
Author(s):  
Erhan Sezerer ◽  
Samet Tenekeci ◽  
Ali Acar ◽  
Bora Baloğlu ◽  
Selma Tekir
Keyword(s):  
Software Engineering ◽  
Design Patterns ◽  
Binary Classification ◽  
Grey Literature ◽  
Language Models ◽  
Superior Performance ◽  
Reputation Measurement ◽  
Objective Quality ◽  
Question And Answer ◽  
Dataset Size

In the field of software engineering, practitioners’ share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors’ mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves [Formula: see text] accuracy in binary classification in SO design patterns tag and [Formula: see text] accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system’s predicted reputation labels match the quality of provided answers.

scholarly journals Neural networks to learn protein sequence-function relationships from deep mutational scanning data

2020 ◽  
Author(s):  
Sam Gelman ◽  
Philip A. Romero ◽  
Anthony Gitter
Keyword(s):  
Protein Structure ◽  
Protein Sequence ◽  
Internal Representation ◽  
Superior Performance ◽  
Network Architectures ◽  
Convolutional Network ◽  
Learning Framework ◽  
And Function ◽  
Multiple Neural Network ◽  
Function Mapping

ABSTRACTThe mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a protein’s behavior and properties. We present a supervised deep learning framework to learn the sequence-function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants. We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a network’s internal representation affects its ability to learn the sequence-function mapping. Our supervised learning approach displays superior performance over physics-based and unsupervised prediction methods. We find networks that capture nonlinear interactions and share parameters across sequence positions are important for learning the relationship between sequence and function. Further analysis of the trained models reveals the networks’ ability to learn biologically meaningful information about protein structure and mechanism. Our software is available from https://github.com/gitter-lab/nn4dms.

scholarly journals Trends in Computer-Aided Diagnosis Using Deep Learning Techniques: A Review of Recent Studies on Algorithm Development

2017 ◽  
Author(s):  
Bosede Iyiade Edwards ◽  
Nosiba Hisham Osman Khougali ◽  
Adrian David Cheok
Keyword(s):  
Deep Learning ◽  
Human Subjects ◽  
Binary Classification ◽  
Computer Aided Diagnosis ◽  
Network Architectures ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Aided ◽  
Multi Class Classification ◽  
Aided Diagnosis

With recent focus on deep neural network architectures for development of algorithms for computer-aided diagnosis (CAD), we provide a review of studies within the last 3 years (2015-2017) reported in selected top journals and conferences. 29 studies that met our inclusion criteria were reviewed to identify trends in this field and to inform future development. Studies have focused mostly on cancer-related diseases within internal medicine while diseases within gender-/age-focused fields like gynaecology/pediatrics have not received much focus. All reviewed studies employed image datasets, mostly sourced from publicly available databases (55.2%) and few based on data from human subjects (31%) and non-medical datasets (13.8%), while CNN architecture was employed in most (70%) of the studies. Confirmation of the effect of data manipulation on quality of output and adoption of multi-class rather than binary classification also require more focus. Future studies should leverage collaborations with medical experts to aid future with actual clinical testing with reporting based on some generally applicable index to enable comparison. Our next steps on plans for CAD development for osteoarthritis (OA), with plans to consider multi-class classification and comparison across deep learning approaches and unsupervised architectures were also highlighted.

Deep Learning-Based Mobile Application for Plant Disease Diagnosis

2019 ◽  
pp. 242-271 ◽  
Author(s):  
Shradha Verma ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Shubham Sharma ◽  
Puranjay Rajvanshi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Plant Disease ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Learning Tools ◽  
Class Labels

With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.

scholarly journals Experimental studies on vegetable marrow seeds ozonation using laboratory equipment

2019 ◽  
Vol 126 ◽  
pp. 00002
Author(s):  
Andrey V. Bogdanov ◽  
Victoria V. Evchenko ◽  
Svetlana Yu. Popova
Keyword(s):  
Seed Treatment ◽  
Treatment Time ◽  
Experimental Studies ◽  
Plant Diseases ◽  
Agricultural Crop ◽  
The Urals ◽  
Laboratory Equipment ◽  
Ozone Concentrations ◽  
Vegetable Marrow ◽  
Sowing Seed

A set of measures is taken to protect plants against various diseases and to prevent yield losses. One of the components of such actions is a pre-sowing seed treatment. One of the effective ways of pre-sowing treatment is seeds ozonization, which stimulates their germination and provides the prevention of plant diseases, hence this process increases the germination and the yield of agricultural crops. Our laboratory equipment was made to substantiate the ozonation regimes. Experimental studies on seeds ozonation were carried out for agricultural crop (the zucchini variety Zebra), which is widely used in Russia, particularly in the natural-climatic areas of the Urals. Taking into account that high ozone concentrations are dangerous to human health and life (to service personnel), pre-sowing seeds treatment was carried out at relatively safe concentrations (up to 10 mg/m3), paying attention to the tightness of the equipment. Ozone concentrations of 3 mg/m3, 6 mg/m3 and 9 mg/m3 and seed treatment time of 120 min, 240 min, 360 min and 480 min were chosen. The experimental studies have shown, that a rational regime of seeds treatment for obtaining the maximum germination (79.1%), is observed when ozone concentration is 9 mg/m3 and treatment time is 247.7 min. Thus, the developed laboratory equipment helps to determine the rational ozonation regimes for zucchini seeds. It can also be used to determine the rational ozonation regimes for seeds of other crops.

scholarly journals Effect of a Strawberry and Spinach Dietary Supplement on Spatial Learning in Early and Late Middle-Aged Female Rats

Antioxidants ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Paula Millin ◽  
Gina Rickert
Keyword(s):  
Dietary Supplement ◽  
Middle Age ◽  
Experimental Studies ◽  
Female Rats ◽  
Superior Performance ◽  
Experimental Investigations ◽  
Standard Diet ◽  
Middle Aged ◽  
Freeze Dried ◽  
Antioxidant Diet

The present experiment sought to determine the effect of an eight-week, high antioxidant, whole-foods dietary supplement on Morris Water Maze performance in early and late middle-aged female rats. To improve ecological validity over past experimental studies, rats in the current study received antioxidants by consuming freeze-dried organic strawberries and spinach rather than by being given food extracts or antioxidant injections. Latency and path length measures both indicated that late middle-aged rats fed the high antioxidant diet performed on a par with the younger animals earlier in training than their standard diet counterparts (p < 0.05). Superior performance was not due to improved fitness in the antioxidant-supplemented rats. Thus, our model showed that a high antioxidant diet of relatively short duration mitigated the mild cognitive decline that was seen in control animals during the developmental period of late middle-age. The current results offer support for the promising role of dietary antioxidants in maintaining cognitive health in normal aging and extend past findings to females, who have been relatively neglected in experimental investigations. Moreover, the current model suggests that the period of transition from early to late middle age is a promising target for dietary intervention in healthy adults.

Experimental Studies on Galling Onset in OCTG Connections: A Review

Tribology ◽  
2006 ◽  
Author(s):  
S. Ekwaro-Osire ◽  
F. Karpat
Keyword(s):  
Natural Gas ◽  
Experimental Studies ◽  
Oil Industry ◽  
Superior Performance ◽  
Review Of Literature ◽  
Performance Properties ◽  
Operational Costs ◽  
Concise Review ◽  
Mode Of Failure ◽  
Very High

With today's high prices for natural gas and oil, the demand for oil and country tubular goods (OCTG), with superior performance properties, is very high. Failures in OCTG can be attributed to numerous sources, for example, makeup torque, corrosion, and galling. Thread galling is the most common mode of failure. This failure often leads to leakage, corrosion of the material, and loss of mechanical integrity. The failure of OCTG eventually amounts to excessive operational costs for the gas and oil industry. The have been numerous approaches taken to improve the galling resistance of OCTG connections. The advocacy of these approaches is often achieved through experimental studies using galling testers. In this paper, it is proposed to classify the galling testers in seven distinct groups. There is a need to design and use effective galling testers to understand and improve the performance of OCTG connections. Thus, the objective of this paper was to present a concise review of literature related to the galling testers that may have applications to OCTG.

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