Comparative Analysis for Improving Accuracy of Image Classification Using Deep Learning Architectures

Vale do São Francisco in Pernambuco is one of the most economically important poles in the state and among its cultivars, it is worth mentioning the grape culture. This sector faces challenges related to the response time between identifying a field infestation and taking corrective actions, in order to minimize losses. This work comprises a comparative analysis between deep learning architectures, applied to identification of diseases in grape cultivars. Results suggest that the use of these technologies is plausible to differentiate healthy grape leaves from leaves presenting one of three different types of diseases, obtaining near 100% accuracy in studied database using an architecture that can be employed in embedded devices.

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Image based flower species classification using CNN

Journal of Innovations in Engineering Education ◽

10.3126/jiee.v2i1.36670 ◽

2019 ◽

Vol 2 (1) ◽

pp. 182-186

Author(s):

Santosh Giri

Keyword(s):

Deep Learning ◽

Image Classification ◽

Training System ◽

Species Classification ◽

Testing Dataset ◽

Machine Learning Applications ◽

Testing Accuracy ◽

Feature Values ◽

Fully Connected ◽

Learning Architectures

Deep learning is one of the essential parts of machine learning. Applications such as image classification, text recognition, object detection etc. used deep learning architectures. In this paper neural network model was designed for image classification. A NN classifier with one fully connected layer and one softmax layer was designed and feature extraction part of inception v3 model was reused to calculate the feature value of each images. And by using these feature values the NN classifier was trained. By adopting transfer learning mechanism NN classifier was trained with 17 classes of oxford 17 flower image dataset. The system provided final training accuracy of 99 %. After training, system was evaluated with testing dataset images. The mean testing accuracy was 86.4%.

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Towards Image Classification with Machine Learning Methodologies for Smartphones

Machine Learning and Knowledge Extraction ◽

10.3390/make1040059 ◽

2019 ◽

Vol 1 (4) ◽

pp. 1039-1057 ◽

Cited By ~ 1

Author(s):

Lili Zhu ◽

Petros Spachos

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Image Classification ◽

Transfer Learning ◽

Time Constraints ◽

Learning Method ◽

Android Application ◽

Optimal Model ◽

Recent Developments ◽

Learning Architectures

Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery.

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A Comparative Analysis of Deep Learning Architectures on High Variation Malaria Parasite Classification Dataset

Tissue and Cell ◽

10.1016/j.tice.2020.101473 ◽

2020 ◽

pp. 101473

Author(s):

Aimon Rahman ◽

Hasib Zunair ◽

Tamanna Rahman Reme ◽

M Sohel Rahman ◽

M.R.C. Mahdy

Keyword(s):

Deep Learning ◽

Comparative Analysis ◽

Malaria Parasite ◽

Learning Architectures

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Deep Learning Based High-Resolution Remote Sensing Image classification

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.384 ◽

2017 ◽

Vol 7 (10) ◽

pp. 22

Author(s):

Sumit Kaur

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Image Classification ◽

Language Processing ◽

Object Perception ◽

Remote Sensing Image ◽

Research Area ◽

Remote Sensing Image Classification ◽

Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification.

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