Plant diseases detection with low resolution data using nested skip connections

Plant diseases detection with low resolution data using nested skip connections

Abstract At the moment, there are increasing trends of using deep learning for plant diseases detection. However, their implementations may be difficult in developing countries due to several reasons. First, existing deep learning models are usually trained with images with adequate resolutions. In...

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Bibliographic Details
Main Authors: Hilman F. Pardede, Endang Suryawati, Vicky Zilvan, Ade Ramdan, R. Budiarianto S. Kusumo, Ana Heryana, R. Sandra Yuwana, Dikdik Krisnandi, Agus Subekti, Fani Fauziah, Vitria P. Rahadi
Format: Article
Language:English
Published: SpringerOpen 2020-08-01
Series:Journal of Big Data
Subjects:
Deep Learning
Convolutional neural network
Light CNN
Plant diseases detection
Tea diseases detection
Online Access:http://link.springer.com/article/10.1186/s40537-020-00332-7

Internet

http://link.springer.com/article/10.1186/s40537-020-00332-7

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