Multi spectral image classification based on deep feature extraction using deep learning technique

2021 ◽  
Vol 17 (3) ◽  
pp. 250
Author(s):  
K. Radhika ◽  
Y. Muralimohanbabu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Image Classification ◽  
Spectral Image ◽  
Deep Feature ◽  
Learning Technique ◽  
Deep Feature Extraction

Smart paddy field monitoring system using deep learning and IoT

2021 ◽  
pp. 1063293X2198894
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Nithiyakanthan Kannan ◽  
Sridevi Narayanan ◽  
Chanki Pandey
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Paddy Field ◽  
New Technology ◽  
Support Vector ◽  
Nitrogen Status ◽  
Deep Feature ◽  
Per Capita ◽  
Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

Supervised Deep Feature Extraction for Hyperspectral Image Classification

2018 ◽  
Vol 56 (4) ◽  
pp. 1909-1921 ◽  
Author(s):  
Bing Liu ◽  
Xuchu Yu ◽  
Pengqiang Zhang ◽  
Anzhu Yu ◽  
Qiongying Fu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Deep Feature ◽  
Deep Feature Extraction

Zero-Shot Image Classification Based on Deep Feature Extraction

2018 ◽  
Vol 10 (2) ◽  
pp. 432-444 ◽  
Author(s):  
Xuesong Wang ◽  
Chen Chen ◽  
Yuhu Cheng ◽  
Z. Jane Wang
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Deep Feature ◽  
Deep Feature Extraction

scholarly journals Deep Feature Extraction and Feature Fusion for Bi-temporal Satellite Image Classification

2020 ◽  
Vol 66 (1) ◽  
pp. 373-388
Author(s):  
Anju Asokan ◽  
J Anitha ◽  
Bogdan Patrut ◽  
Dana Danciulescu ◽  
D Jude Hemanth
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Feature Fusion ◽  
Satellite Image ◽  
Satellite Image Classification ◽  
Deep Feature ◽  
Deep Feature Extraction
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