scholarly journals Implementation of Deep Dictionary Learning and Coding Network for Plant Disease Detection in Agricultural Field

2021 ◽  
pp. 547-553
Author(s):  
Snehal A. Lale ◽  
Dr. V. K. Shandilya
Keyword(s):  
Deep Learning ◽  
Dictionary Learning ◽  
Plant Disease ◽  
Sparse Matrix ◽  
Agricultural Field ◽  
Plant Leaves ◽  
End User ◽  
Data Set ◽  
Online Application ◽  
Web Based

A web based online application is built to detect the diseases present on plant leaves. This will help farmer/end user to find out which diseases are present on that leaves. In older times, this task requires much time & resources to do the detection. A approach has of DDLCN has been taken in this project which will help to find out the best match features. In this approach. I have taken up to 3 layers of DDLCN which will filter the most match features of the diseases present on plant leaves. The proposed DDLCN combines the two things that is deep learning & dictionary learning. Deep learning which the branch of AI. It has a structures inspired by the human brain. It has the artificial neural networks which helps to train the data. Now taking about the dictionary learning which has a literal meaning having large number of data set. Dictionary learning is also called as sparse representation which will help to arrange the data in proper manner and without wasting the storage. Because it will only count the non zeros numbers present in the sparse matrix.

scholarly journals Predicting Groundnut Disease using CNN Models

2021 ◽  
Vol 23 (06) ◽  
pp. 756-766
Author(s):  
Neha Suresh ◽  
◽  
Dr.AnandiGiridharan Dr.AnandiGiridharan ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Arachis Hypogaea ◽  
Efficient Method ◽  
Learning Process ◽  
Plant Disease ◽  
Disease Classification ◽  
Agricultural Field ◽  
Arachis Hypogaea L ◽  
Overall Performance

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. Groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root, and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of convolutional neural network (CNN) because it automatically detects the important features without any human supervision. The proposed methodology can deeply detect plant disease by using a deep learning process. Ultimately, the groundnut disease classification with its overall performance of the proposed methodology provides 96% accuracy.

scholarly journals Potato Leaf Disease Classification using Transfer Learning based Modified Xception Model

2020 ◽  
pp. 438-442
Author(s):  
Rajasekaran Thangaraj ◽  
Pandiyan P ◽  
Vishnu Kumar Kaliappan ◽  
Anandamurugan S ◽  
Indupriya P
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Potato Plant ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Selection Criterion ◽  
Plant Diseases ◽  
Disease Classification ◽  
Agricultural Field ◽  
Potato Leaf

Plant diseases are the essential thing which decreases the quantity as well quality in agricultural field. As a result, the identification and analysis of the diseases are important. The proper classification with least data in deep learning is the most challenging task. In addition, it is tough to label the data manually depending upon the selection criterion. Transfer learning algorithm helps in resolving this kind of problem by means of learning the previous task and then applying capabilities and knowledge to the new task. This work presents the convolution neural network-based model to predict and analysis the potato plant disease using plant village datasets with deep learning algorithms. Transfer learning with feature extraction model is employed to detect the potato plant disease. The results show that improved performance with an accuracy of 98.16%, precision of 98.18%, the recall value of 98.17% and the F1 score value of 98.169 %.

scholarly journals High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Yaser AbdulAali Jasim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Plant Disease ◽  
Detection System ◽  
Open Data ◽  
Data Set ◽  
Performance Rate

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence.  Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers. In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.

Deep Learning

2019 ◽  
pp. 108-134
Author(s):  
Amit Sinha ◽  
Suneet Kumar Gupta ◽  
Anurag Tiwari ◽  
Amrita Chaturvedi
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Plant Disease ◽  
Learning Algorithms ◽  
The Other ◽  
Learning Approaches ◽  
Agricultural Field ◽  
Practical Applications ◽  
Other Hand

Deep learning approaches have been found to be suitable for the agricultural field with successful applications to vegetable infection through plant disease. In this chapter, the authors discuss some widely used deep learning architecture and their practical applications. Nowadays, in many typical applications of machine vision, there is a tendency to replace classical techniques with deep learning algorithms. The benefits are valuable; on one hand, it avoids the need of specialized handcrafted features extractors, and on the other hand, results are not damaged. Moreover, they typically get improved.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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