scholarly journals Recognition of Image-Based Plant Leaf Diseases Using Deep Learning Classification Models

2021 ◽  
Vol 20 (5) ◽  
Author(s):  
Sakshi Takkar ◽  
Anuj Kakran ◽  
Veerpal Kaur ◽  
Manik Rakhra ◽  
Manish Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Agricultural Land ◽  
Super Resolution ◽  
Modern Technology ◽  
Primary Objective ◽  
Plant Diseases ◽  
Learning Approaches ◽  
Agricultural Field ◽  
Detection And Identification ◽  
The Individual

Plant diseases are spread by a variety of pests, weeds, and pathogens and may have a devastating effect on agriculture, if not handled in a timely manner. Farmers face umpteen challenges from a proper water supply, untimely rain, storage facilities, and several plant diseases. Crops disease is the primary threat and it causes enormous loss to farmers in terms of production and finance. Identifying the disease from several hectares of agricultural land is a very difficult practice even with the presence of modern technology. Accurate and rapid illness prediction for early illness treatment to crops minimizes economical loss to the individual and further proves to be productive for healthy crops. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. The suggested method notifies farmers of different agricultural illnesses, prompting them to take further essential precautions before the disease spreads to the whole agricultural field. The primary objective of this study is to detect the illnesses as soon as they begin to spread on the leaves of the plants. Super-Resolution Convolutional Neural Network (SRCNN) and Bicubic models are employed in the system to identify healthy and diseased leaves with an accuracy of 99.175 % and 99.156 % respectively.

scholarly journals Super-resolution for Ocean Bathymetric Maps Using Deep Learning Approaches : A Comparison and Validation

2021 ◽  
Vol 32 (1) ◽  
pp. 3-13
Author(s):  
Mitsuko HIDAKA ◽  
Daisuke MATSUOKA ◽  
Tatsu KUWATANI ◽  
Junji KANEKO ◽  
Takafumi KASAYA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Learning Approaches

scholarly journals Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3929 ◽  
Author(s):  
Grigorios Tsagkatakis ◽  
Anastasia Aidini ◽  
Konstantina Fotiadou ◽  
Michalis Giannopoulos ◽  
Anastasia Pentari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Body ◽  
Super Resolution ◽  
Imaging Systems ◽  
Learning Approaches ◽  
Language Understanding ◽  
Learning Tasks ◽  
Trade Offs ◽  
Sensing Platforms

Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

A literature review of localization methods and emergence of deep learning in single-molecule localization microscopy

2020 ◽  
Author(s):  
Anish Mukherjee
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Super Resolution ◽  
Point Sources ◽  
Learning Approaches ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Trade Offs ◽  
Emitter Localization ◽  
Single Molecule Localization Microscopy

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.

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 Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
YiNan Zhang ◽  
MingQiang An
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Medical Images ◽  
Super Resolution ◽  
Training Dataset ◽  
Reconstruction Method ◽  
Hybrid Architecture ◽  
Learning Approaches ◽  
Training Samples ◽  
Bicubic Interpolation

Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.

RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database

2019 ◽  
Vol 100 ◽  
pp. 86-97 ◽  
Author(s):  
Mohammad F. Al-Sa’d ◽  
Abdulla Al-Ali ◽  
Amr Mohamed ◽  
Tamer Khattab ◽  
Aiman Erbad
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Learning Approaches ◽  
Detection And Identification

scholarly journals Deep learning approaches for real-time image super-resolution

2020 ◽  
Vol 32 (18) ◽  
pp. 14519-14520
Author(s):  
Pourya Shamsolmoali ◽  
M. Emre Celebi ◽  
Ruili Wang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Learning Approaches ◽  
Real Time Image ◽  
Image Super Resolution ◽  
Time Image

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.

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