Deep learning for detection cassava leaf disease

In this research, an intelligent system for detecting cassava leaf disease has been developed by utilizing the MobileNetV2 deep learning model and displaying it using a python graphical user interface (GUI). There are five disease classes used in this study, namely Cassava Bacterial Blight (CBB), Ca...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2021-01, Vol.1751 (1), p.12072
Main Authors: Ayu, H R, Surtono, A, Apriyanto, D K
Format: Article
Language:English
Subjects:
Blight
Cassava
Deep learning
Graphical user interface
Physics
Plant diseases
User interface
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this research, an intelligent system for detecting cassava leaf disease has been developed by utilizing the MobileNetV2 deep learning model and displaying it using a python graphical user interface (GUI). There are five disease classes used in this study, namely Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD) and Healthy. The results showed that the overall accuracy of the test data obtained was 65,6%. The GUI application program was made to be operated efficiently for beginners and can be used by cassava farmers in the field.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1751/1/012072